yng <- read.delim("results_young.txt", stringsAsFactors=TRUE)
yng <- yng[, -c(12,13,19,20)]
yng
## participant age gender syllable register vowel Duration F1_midpoint
## 1 P10 24 female open high a 341 1230
## 2 P10 24 female open high a 278 1146
## 3 P10 24 female open low a 338 1188
## 4 P10 24 female open low a 344 1087
## 5 P10 24 female open high low_o 403 521
## 6 P10 24 female open high low_o 314 528
## 7 P10 24 female open low low_o 383 578
## 8 P10 24 female open low low_o 332 551
## 9 P10 24 female open high ou 403 418
## 10 P10 24 female open high ou 339 304
## 11 P10 24 female open low ou 361 337
## 12 P10 24 female open low ou 385 361
## 13 P10 24 female open high unround_o 428 405
## 14 P10 24 female open high unround_o 371 413
## 15 P10 24 female open low unround_o 329 485
## 16 P10 24 female open low unround_o 347 505
## 17 P10 24 female open high e 300 387
## 18 P10 24 female open high e 336 576
## 19 P10 24 female open low e 361 518
## 20 P10 24 female open low e 318 533
## 21 P10 24 female open high E 376 490
## 22 P10 24 female open high E 320 511
## 23 P10 24 female open low E 332 500
## 24 P10 24 female open low E 363 497
## 25 P10 24 female open high round_e 396 401
## 26 P10 24 female open high round_e 355 406
## 27 P10 24 female open low round_e 296 355
## 28 P10 24 female open low round_e 293 362
## 29 P10 24 female open high i 429 331
## 30 P10 24 female open high i 375 413
## 31 P10 24 female open low i 359 244
## 32 P10 24 female open low i 347 405
## 33 P10 24 female open high u 338 373
## 34 P10 24 female open high u 402 351
## 35 P10 24 female open low u 395 365
## 36 P10 24 female open low u 308 416
## 37 P10 24 female open high y 354 351
## 38 P10 24 female open high y 290 313
## 39 P10 24 female open low y 314 326
## 40 P10 24 female open low y 261 343
## 41 P10 24 female closed high A 133 838
## 42 P10 24 female closed high A 162 918
## 43 P10 24 female closed low A 197 988
## 44 P10 24 female closed low A 125 904
## 45 P10 24 female closed high o 159 502
## 46 P10 24 female closed high o 247 510
## 47 P10 24 female closed low o 250 562
## 48 P10 24 female closed low o 202 655
## 49 P10 24 female closed high schwa 176 955
## 50 P10 24 female closed high schwa 158 952
## 51 P10 24 female closed low schwa 221 991
## 52 P10 24 female closed high I 128 533
## 53 P10 24 female closed high I 142 547
## 54 P10 24 female closed low I 243 517
## 55 P10 24 female closed low I 287 660
## 56 P10 24 female closed high Y 149 496
## 57 P10 24 female closed high Y 159 532
## 58 P10 24 female closed low Y 222 823
## 59 P10 24 female closed low Y 271 772
## 60 P15 24 female open high a 384 1026
## 61 P15 24 female open high a 358 1056
## 62 P15 24 female open low a 464 911
## 63 P15 24 female open low a 461 1016
## 64 P15 24 female open high low_o 525 503
## 65 P15 24 female open high low_o 392 666
## 66 P15 24 female open low low_o 439 561
## 67 P15 24 female open low low_o 403 590
## 68 P15 24 female open high ou 431 478
## 69 P15 24 female open high ou 452 319
## 70 P15 24 female open low ou 442 367
## 71 P15 24 female open low ou 398 469
## 72 P15 24 female open high unround_o 311 487
## 73 P15 24 female open high unround_o 366 501
## 74 P15 24 female open low unround_o 386 499
## 75 P15 24 female open low unround_o 343 547
## 76 P15 24 female open high e 377 496
## 77 P15 24 female open high e 315 548
## 78 P15 24 female open low e 444 460
## 79 P15 24 female open low e 400 467
## 80 P15 24 female open high E 344 627
## 81 P15 24 female open high E 513 528
## 82 P15 24 female open low E 376 628
## 83 P15 24 female open low E 424 491
## 84 P15 24 female open high round_e 396 433
## 85 P15 24 female open high round_e 355 447
## 86 P15 24 female open low round_e 422 397
## 87 P15 24 female open low round_e 373 404
## 88 P15 24 female open high i 496 432
## 89 P15 24 female open high i 453 424
## 90 P15 24 female open low i 418 296
## 91 P15 24 female open low i 498 250
## 92 P15 24 female open high u 405 322
## 93 P15 24 female open high u 358 506
## 94 P15 24 female open low u 228 391
## 95 P15 24 female open low u 375 404
## 96 P15 24 female open high y 358 462
## 97 P15 24 female open high y 364 461
## 98 P15 24 female open low y 380 295
## 99 P15 24 female open low y 328 404
## 100 P15 24 female closed high A 98 661
## 101 P15 24 female closed high A 128 914
## 102 P15 24 female closed low A 270 803
## 103 P15 24 female closed low A 224 808
## 104 P15 24 female closed high o 135 555
## 105 P15 24 female closed high o 87 579
## 106 P15 24 female closed low o 186 711
## 107 P15 24 female closed low o 195 509
## 108 P15 24 female closed high schwa 68 646
## 109 P15 24 female closed high schwa 150 779
## 110 P15 24 female closed low schwa 263 789
## 111 P15 24 female closed high I 120 593
## 112 P15 24 female closed high I 145 575
## 113 P15 24 female closed low I 188 623
## 114 P15 24 female closed low I 217 658
## 115 P15 24 female closed high Y 151 533
## 116 P15 24 female closed high Y 145 585
## 117 P15 24 female closed low Y 277 668
## 118 P15 24 female closed low Y 126 587
## 119 P18 21 male open high a 231 770
## 120 P18 21 male open low a 206 800
## 121 P18 21 male open low a 282 695
## 122 P18 21 male open high low_o 243 527
## 123 P18 21 male open high low_o 264 537
## 124 P18 21 male open low low_o 243 510
## 125 P18 21 male open low low_o 277 574
## 126 P18 21 male open high ou 282 372
## 127 P18 21 male open high ou 276 278
## 128 P18 21 male open low ou 223 347
## 129 P18 21 male open low ou 236 359
## 130 P18 21 male open high unround_o 213 445
## 131 P18 21 male open high unround_o 207 417
## 132 P18 21 male open low unround_o 228 448
## 133 P18 21 male open low unround_o 289 458
## 134 P18 21 male open high e 231 376
## 135 P18 21 male open high e 245 394
## 136 P18 21 male open low e 242 428
## 137 P18 21 male open low e 271 423
## 138 P18 21 male open high E 254 525
## 139 P18 21 male open high E 243 469
## 140 P18 21 male open low E 204 522
## 141 P18 21 male open low E 188 501
## 142 P18 21 male open high round_e 209 343
## 143 P18 21 male open high round_e 264 336
## 144 P18 21 male open low round_e 243 360
## 145 P18 21 male open low round_e 236 345
## 146 P18 21 male open high i 263 234
## 147 P18 21 male open high i 208 271
## 148 P18 21 male open low i 241 350
## 149 P18 21 male open low i 214 283
## 150 P18 21 male open high u 207 397
## 151 P18 21 male open high u 223 341
## 152 P18 21 male open low u 207 396
## 153 P18 21 male open high y 196 383
## 154 P18 21 male open low y 273 422
## 155 P18 21 male closed high A 124 572
## 156 P18 21 male closed high A 88 605
## 157 P18 21 male closed low A 124 584
## 158 P18 21 male closed low A 138 647
## 159 P18 21 male closed high o 98 508
## 160 P18 21 male closed high o 194 499
## 161 P18 21 male closed low o 176 568
## 162 P18 21 male closed low o 270 559
## 163 P18 21 male closed high schwa 133 581
## 164 P18 21 male closed high schwa 108 610
## 165 P18 21 male closed low schwa 137 589
## 166 P18 21 male closed low schwa 125 549
## 167 P18 21 male closed high I 120 456
## 168 P18 21 male closed high I 115 480
## 169 P18 21 male closed low I 141 495
## 170 P18 21 male closed low I 150 517
## 171 P18 21 male closed high Y 112 460
## 172 P18 21 male closed high Y 143 521
## 173 P18 21 male closed low Y 119 535
## 174 P18 21 male closed low Y 109 453
## 175 P23 21 male open high a 370 908
## 176 P23 21 male open high a 343 977
## 177 P23 21 male open low a 423 940
## 178 P23 21 male open low a 338 983
## 179 P23 21 male open high low_o 376 423
## 180 P23 21 male open high low_o 376 516
## 181 P23 21 male open low low_o 398 652
## 182 P23 21 male open low low_o 405 511
## 183 P23 21 male open high ou 376 312
## 184 P23 21 male open high ou 348 307
## 185 P23 21 male open low ou 396 351
## 186 P23 21 male open low ou 409 409
## 187 P23 21 male open high unround_o 394 448
## 188 P23 21 male open high unround_o 406 336
## 189 P23 21 male open low unround_o 459 378
## 190 P23 21 male open low unround_o 311 469
## 191 P23 21 male open high e 302 401
## 192 P23 21 male open high e 339 420
## 193 P23 21 male open low e 408 355
## 194 P23 21 male open low e 343 389
## 195 P23 21 male open high E 327 482
## 196 P23 21 male open high E 323 485
## 197 P23 21 male open low E 431 540
## 198 P23 21 male open low E 316 485
## 199 P23 21 male open high round_e 338 361
## 200 P23 21 male open high round_e 318 339
## 201 P23 21 male open low round_e 390 332
## 202 P23 21 male open low round_e 351 296
## 203 P23 21 male open high i 321 271
## 204 P23 21 male open high i 349 271
## 205 P23 21 male open low i 298 291
## 206 P23 21 male open low i 405 330
## 207 P23 21 male open high u 396 313
## 208 P23 21 male open high u 344 332
## 209 P23 21 male open low u 317 369
## 210 P23 21 male open high y 342 335
## 211 P23 21 male open high y 280 324
## 212 P23 21 male closed high A 100 819
## 213 P23 21 male closed high A 111 842
## 214 P23 21 male closed low A 134 673
## 215 P23 21 male closed low A 161 805
## 216 P23 21 male closed high o 139 554
## 217 P23 21 male closed high o 103 495
## 218 P23 21 male closed low o 98 673
## 219 P23 21 male closed low o 132 568
## 220 P23 21 male closed high schwa 73 698
## 221 P23 21 male closed high schwa 77 701
## 222 P23 21 male closed low schwa 73 692
## 223 P23 21 male closed high I 90 418
## 224 P23 21 male closed high I 94 434
## 225 P23 21 male closed low I 148 468
## 226 P23 21 male closed low I 142 478
## 227 P23 21 male closed low Y 105 513
## 228 P23 21 male closed low Y 115 502
## 229 P25 24 female open high a 430 1127
## 230 P25 24 female open high a 553 1181
## 231 P25 24 female open low a 546 1123
## 232 P25 24 female open low a 480 1253
## 233 P25 24 female open high low_o 453 554
## 234 P25 24 female open high low_o 458 693
## 235 P25 24 female open low low_o 444 588
## 236 P25 24 female open low low_o 314 550
## 237 P25 24 female open high ou 394 457
## 238 P25 24 female open high ou 616 491
## 239 P25 24 female open low ou 407 460
## 240 P25 24 female open high unround_o 382 535
## 241 P25 24 female open high unround_o 394 521
## 242 P25 24 female open low unround_o 450 432
## 243 P25 24 female open low unround_o 334 480
## 244 P25 24 female open high e 410 496
## 245 P25 24 female open high e 368 500
## 246 P25 24 female open low e 484 480
## 247 P25 24 female open high E 434 656
## 248 P25 24 female open high E 374 544
## 249 P25 24 female open low E 372 511
## 250 P25 24 female open low E 390 628
## 251 P25 24 female open high round_e 425 486
## 252 P25 24 female open high round_e 397 476
## 253 P25 24 female open low round_e 456 370
## 254 P25 24 female open low round_e 276 455
## 255 P25 24 female open high i 344 459
## 256 P25 24 female open high i 335 481
## 257 P25 24 female open low i 489 415
## 258 P25 24 female open low i 509 428
## 259 P25 24 female open high u 314 492
## 260 P25 24 female open high u 396 521
## 261 P25 24 female open low u 332 417
## 262 P25 24 female open high y 476 486
## 263 P25 24 female open high y 255 428
## 264 P25 24 female open low y 338 401
## 265 P25 24 female closed high A 103 981
## 266 P25 24 female closed high A 124 965
## 267 P25 24 female closed low A 172 783
## 268 P25 24 female closed low A 134 897
## 269 P25 24 female closed high o 160 679
## 270 P25 24 female closed high o 100 693
## 271 P25 24 female closed low o 169 692
## 272 P25 24 female closed low o 152 705
## 273 P25 24 female closed high schwa 113 526
## 274 P25 24 female closed high schwa 77 663
## 275 P25 24 female closed low schwa 113 784
## 276 P25 24 female closed high I 113 655
## 277 P25 24 female closed high I 116 614
## 278 P25 24 female closed low I 97 560
## 279 P25 24 female closed low I 132 536
## 280 P25 24 female closed high Y 129 514
## 281 P25 24 female closed low Y 161 723
## 282 P25 24 female closed low Y 192 715
## 283 P222 23 female open high a 383 933
## 284 P222 23 female open high a 373 787
## 285 P222 23 female open high low_o 385 572
## 286 P222 23 female open high low_o 442 636
## 287 P222 23 female open high unround_o 358 498
## 288 P222 23 female open high unround_o 404 531
## 289 P222 23 female open low unround_o 295 504
## 290 P222 23 female open low unround_o 328 526
## 291 P222 23 female open high e 456 544
## 292 P222 23 female open high e 370 484
## 293 P222 23 female open low e 391 443
## 294 P222 23 female open low e 481 458
## 295 P222 23 female open high E 347 635
## 296 P222 23 female open high E 364 614
## 297 P222 23 female open low E 406 588
## 298 P222 23 female open high round_e 349 509
## 299 P222 23 female open high round_e 377 403
## 300 P222 23 female open low y 327 397
## 301 P222 23 female open low y 333 425
## 302 P222 23 female closed high A 112 868
## 303 P222 23 female closed high A 142 881
## 304 P222 23 female closed high o 104 582
## 305 P222 23 female closed high o 102 592
## 306 P222 23 female closed high schwa 230 654
## 307 P222 23 female closed high schwa 97 636
## 308 P222 23 female closed low schwa 202 621
## 309 P222 23 female closed high I 107 523
## 310 P222 23 female closed high I 94 588
## 311 P222 23 female closed low I 123 481
## 312 P222 23 female closed high Y 90 566
## 313 P222 23 female closed high Y 139 582
## 314 P222 23 female closed low Y 136 465
## 315 P234 24 female open high a 288 968
## 316 P234 24 female open high a 268 1029
## 317 P234 24 female open low a 443 1000
## 318 P234 24 female open low a 304 1062
## 319 P234 24 female open high low_o 373 782
## 320 P234 24 female open low low_o 326 707
## 321 P234 24 female open low low_o 274 646
## 322 P234 24 female open high ou 352 392
## 323 P234 24 female open low ou 350 412
## 324 P234 24 female open low ou 292 346
## 325 P234 24 female open high unround_o 356 566
## 326 P234 24 female open high unround_o 403 496
## 327 P234 24 female open low unround_o 335 602
## 328 P234 24 female open low unround_o 342 519
## 329 P234 24 female open high e 333 432
## 330 P234 24 female open high e 333 430
## 331 P234 24 female open low e 287 464
## 332 P234 24 female open low e 325 462
## 333 P234 24 female open high E 292 532
## 334 P234 24 female open high E 339 526
## 335 P234 24 female open low E 299 577
## 336 P234 24 female open low E 295 583
## 337 P234 24 female open high round_e 340 390
## 338 P234 24 female open high round_e 345 378
## 339 P234 24 female open low round_e 307 398
## 340 P234 24 female open high i 381 287
## 341 P234 24 female open high i 317 280
## 342 P234 24 female open low i 282 259
## 343 P234 24 female open low i 303 266
## 344 P234 24 female open high u 330 307
## 345 P234 24 female open high u 304 437
## 346 P234 24 female open low u 260 398
## 347 P234 24 female open low u 305 352
## 348 P234 24 female open high y 341 310
## 349 P234 24 female open high y 371 308
## 350 P234 24 female open low y 257 270
## 351 P234 24 female closed high A 145 916
## 352 P234 24 female closed high A 179 927
## 353 P234 24 female closed low A 197 1048
## 354 P234 24 female closed high o 168 687
## 355 P234 24 female closed high o 235 622
## 356 P234 24 female closed low o 219 632
## 357 P234 24 female closed high schwa 200 902
## 358 P234 24 female closed high schwa 149 782
## 359 P234 24 female closed low schwa 236 841
## 360 P234 24 female closed high I 179 487
## 361 P234 24 female closed low I 126 568
## 362 P234 24 female closed low I 205 627
## 363 P234 24 female closed high Y 225 713
## 364 P234 24 female closed high Y 122 621
## 365 P234 24 female closed low Y 132 640
## 366 P234 24 female closed low Y 198 681
## 367 P1 24 male open high a 418 820
## 368 P1 24 male open high a 467 848
## 369 P1 24 male open low a 486 1054
## 370 P1 24 male open low a 483 1113
## 371 P1 24 male open high low_o 459 464
## 372 P1 24 male open high low_o 352 564
## 373 P1 24 male open low low_o 524 394
## 374 P1 24 male open low low_o 525 539
## 375 P1 24 male open high ou 482 351
## 376 P1 24 male open high ou 458 330
## 377 P1 24 male open low ou 446 292
## 378 P1 24 male open low ou 474 320
## 379 P1 24 male open high unround_o 429 411
## 380 P1 24 male open high unround_o 438 428
## 381 P1 24 male open low unround_o 391 564
## 382 P1 24 male open low unround_o 490 571
## 383 P1 24 male open high e 447 352
## 384 P1 24 male open high e 414 396
## 385 P1 24 male open low e 500 605
## 386 P1 24 male open low e 389 494
## 387 P1 24 male open high E 413 454
## 388 P1 24 male open high E 394 492
## 389 P1 24 male open low E 376 508
## 390 P1 24 male open low E 409 524
## 391 P1 24 male open high round_e 424 369
## 392 P1 24 male open high round_e 427 308
## 393 P1 24 male open low round_e 400 360
## 394 P1 24 male open low round_e 474 341
## 395 P1 24 male open high i 399 265
## 396 P1 24 male open high i 425 267
## 397 P1 24 male open low i 460 227
## 398 P1 24 male open low i 476 218
## 399 P1 24 male open high u 399 276
## 400 P1 24 male open high u 463 330
## 401 P1 24 male open low u 417 283
## 402 P1 24 male open low u 458 294
## 403 P1 24 male open high y 462 258
## 404 P1 24 male open high y 411 278
## 405 P1 24 male open low y 341 280
## 406 P1 24 male open low y 448 267
## 407 P1 24 male closed high A 106 793
## 408 P1 24 male closed high A 95 751
## 409 P1 24 male closed low A 147 713
## 410 P1 24 male closed low A 197 867
## 411 P1 24 male closed high o 114 518
## 412 P1 24 male closed high o 102 553
## 413 P1 24 male closed low o 166 552
## 414 P1 24 male closed low o 151 559
## 415 P1 24 male closed high schwa 101 765
## 416 P1 24 male closed high schwa 121 693
## 417 P1 24 male closed low schwa 223 647
## 418 P1 24 male closed low schwa 187 761
## 419 P1 24 male closed high I 97 422
## 420 P1 24 male closed high I 117 476
## 421 P1 24 male closed low I 202 472
## 422 P1 24 male closed low I 167 489
## 423 P1 24 male closed high Y 107 544
## 424 P1 24 male closed high Y 97 536
## 425 P1 24 male closed low Y 158 629
## 426 P1 24 male closed low Y 212 638
## 427 P19 24 female open high a 328 1090
## 428 P19 24 female open high a 364 1065
## 429 P19 24 female open low a 427 1180
## 430 P19 24 female open low a 489 1202
## 431 P19 24 female open high low_o 373 644
## 432 P19 24 female open high low_o 341 639
## 433 P19 24 female open low low_o 346 498
## 434 P19 24 female open high ou 430 545
## 435 P19 24 female open high ou 296 585
## 436 P19 24 female open low ou 399 532
## 437 P19 24 female open low ou 389 534
## 438 P19 24 female open high unround_o 254 558
## 439 P19 24 female open high unround_o 298 549
## 440 P19 24 female open low unround_o 387 481
## 441 P19 24 female open low unround_o 431 554
## 442 P19 24 female open high e 287 480
## 443 P19 24 female open high e 284 499
## 444 P19 24 female open low e 353 500
## 445 P19 24 female open high E 298 643
## 446 P19 24 female open high E 283 568
## 447 P19 24 female open low E 388 570
## 448 P19 24 female open low E 422 584
## 449 P19 24 female open high round_e 291 420
## 450 P19 24 female open high round_e 341 429
## 451 P19 24 female open low round_e 362 375
## 452 P19 24 female open low round_e 260 375
## 453 P19 24 female open high i 432 313
## 454 P19 24 female open low i 464 334
## 455 P19 24 female open low i 472 348
## 456 P19 24 female open high u 370 382
## 457 P19 24 female open high u 252 415
## 458 P19 24 female open low u 378 366
## 459 P19 24 female open low u 385 356
## 460 P19 24 female open high y 381 558
## 461 P19 24 female open high y 269 412
## 462 P19 24 female closed high A 89 922
## 463 P19 24 female closed high A 101 941
## 464 P19 24 female closed high o 98 514
## 465 P19 24 female closed high o 106 763
## 466 P19 24 female closed high schwa 108 802
## 467 P19 24 female closed high schwa 98 815
## 468 P19 24 female closed low schwa 120 613
## 469 P19 24 female closed high I 98 577
## 470 P19 24 female closed high I 101 569
## 471 P19 24 female closed low I 135 483
## 472 P19 24 female closed low I 149 450
## 473 P19 24 female closed high Y 102 568
## 474 P19 24 female closed high Y 102 455
## 475 P19 24 female closed low Y 120 517
## 476 P19 24 female closed low Y 175 521
## 477 P20 24 female open high a 341 849
## 478 P20 24 female open high a 325 767
## 479 P20 24 female open low a 343 870
## 480 P20 24 female open low a 307 1029
## 481 P20 24 female open high low_o 336 507
## 482 P20 24 female open high low_o 298 533
## 483 P20 24 female open low low_o 309 567
## 484 P20 24 female open low low_o 354 403
## 485 P20 24 female open high ou 344 183
## 486 P20 24 female open high ou 399 314
## 487 P20 24 female open low ou 307 402
## 488 P20 24 female open low ou 370 422
## 489 P20 24 female open high unround_o 331 481
## 490 P20 24 female open high unround_o 320 473
## 491 P20 24 female open low unround_o 224 575
## 492 P20 24 female open low unround_o 345 450
## 493 P20 24 female open high e 297 509
## 494 P20 24 female open high e 323 465
## 495 P20 24 female open high E 349 482
## 496 P20 24 female open high E 375 513
## 497 P20 24 female open low E 294 470
## 498 P20 24 female open low E 258 619
## 499 P20 24 female open high round_e 366 318
## 500 P20 24 female open high round_e 359 354
## 501 P20 24 female open low round_e 307 368
## 502 P20 24 female open low round_e 291 427
## 503 P20 24 female open high i 425 406
## 504 P20 24 female open high i 371 279
## 505 P20 24 female open low i 255 288
## 506 P20 24 female open low i 352 358
## 507 P20 24 female open high u 280 278
## 508 P20 24 female open high u 293 467
## 509 P20 24 female open low u 255 414
## 510 P20 24 female open low u 257 315
## 511 P20 24 female open high y 325 402
## 512 P20 24 female open high y 366 273
## 513 P20 24 female open low y 349 373
## 514 P20 24 female closed high A 94 732
## 515 P20 24 female closed high A 144 768
## 516 P20 24 female closed low A 151 801
## 517 P20 24 female closed low A 128 780
## 518 P20 24 female closed high o 177 536
## 519 P20 24 female closed high o 220 428
## 520 P20 24 female closed low o 204 610
## 521 P20 24 female closed high schwa 94 676
## 522 P20 24 female closed high schwa 79 673
## 523 P20 24 female closed low schwa 146 779
## 524 P20 24 female closed high I 150 552
## 525 P20 24 female closed high I 140 585
## 526 P20 24 female closed low I 161 494
## 527 P20 24 female closed high Y 122 544
## 528 P20 24 female closed high Y 134 494
## 529 P20 24 female closed low Y 146 508
## 530 P20 24 female closed low Y 162 515
## 531 P220 16 male open high a 254 825
## 532 P220 16 male open low a 277 856
## 533 P220 16 male open low a 265 834
## 534 P220 16 male open high low_o 307 565
## 535 P220 16 male open low low_o 259 536
## 536 P220 16 male open high ou 260 342
## 537 P220 16 male open high ou 354 290
## 538 P220 16 male open low ou 291 365
## 539 P220 16 male open high unround_o 304 406
## 540 P220 16 male open low unround_o 388 427
## 541 P220 16 male open high e 135 369
## 542 P220 16 male open low e 289 456
## 543 P220 16 male open high E 503 559
## 544 P220 16 male open high E 349 553
## 545 P220 16 male open low E 226 575
## 546 P220 16 male open high round_e 306 337
## 547 P220 16 male open high round_e 255 359
## 548 P220 16 male open low round_e 309 355
## 549 P220 16 male open low round_e 298 296
## 550 P220 16 male open high i 273 314
## 551 P220 16 male open low u 361 283
## 552 P220 16 male open low u 304 330
## 553 P220 16 male open high y 376 322
## 554 P220 16 male open high y 284 260
## 555 P220 16 male open low y 307 221
## 556 P220 16 male closed low A 107 717
## 557 P220 16 male closed high o 89 478
## 558 P220 16 male closed high o 87 521
## 559 P220 16 male closed low o 127 538
## 560 P220 16 male closed low o 123 586
## 561 P220 16 male closed high schwa 121 700
## 562 P220 16 male closed low schwa 120 766
## 563 P220 16 male closed low schwa 125 714
## 564 P220 16 male closed low I 115 476
## 565 P220 16 male closed high Y 80 544
## 566 P220 16 male closed high Y 111 489
## 567 P220 16 male closed low Y 96 477
## 568 P220 16 male closed low Y 92 458
## 569 P250 25 male open high a 332 852
## 570 P250 25 male open high a 270 909
## 571 P250 25 male open low a 378 1039
## 572 P250 25 male open low a 309 1043
## 573 P250 25 male open high low_o 311 410
## 574 P250 25 male open high low_o 317 460
## 575 P250 25 male open low low_o 324 514
## 576 P250 25 male open low low_o 319 504
## 577 P250 25 male open high ou 316 312
## 578 P250 25 male open high ou 297 325
## 579 P250 25 male open low ou 360 296
## 580 P250 25 male open low ou 361 350
## 581 P250 25 male open high unround_o 292 680
## 582 P250 25 male open high unround_o 304 692
## 583 P250 25 male open low unround_o 357 493
## 584 P250 25 male open low unround_o 306 329
## 585 P250 25 male open high e 383 309
## 586 P250 25 male open high e 302 437
## 587 P250 25 male open low e 364 377
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## 1145 P39 23 female open high round_e 303 409
## 1146 P39 23 female open low round_e 343 385
## 1147 P39 23 female open low round_e 301 396
## 1148 P39 23 female open high i 372 317
## 1149 P39 23 female open high i 420 354
## 1150 P39 23 female open low i 296 448
## 1151 P39 23 female open low i 304 349
## 1152 P39 23 female open high u 247 470
## 1153 P39 23 female open high u 325 447
## 1154 P39 23 female open low u 291 216
## 1155 P39 23 female open low u 332 443
## 1156 P39 23 female open high y 340 399
## 1157 P39 23 female open high y 369 387
## 1158 P39 23 female open low y 366 277
## 1159 P39 23 female open low y 389 384
## 1160 P39 23 female closed high A 89 837
## 1161 P39 23 female closed high A 107 869
## 1162 P39 23 female closed low A 133 747
## 1163 P39 23 female closed low A 168 708
## 1164 P39 23 female closed high o 116 717
## 1165 P39 23 female closed high o 209 763
## 1166 P39 23 female closed low o 146 742
## 1167 P39 23 female closed low o 126 714
## 1168 P39 23 female closed high schwa 212 587
## 1169 P39 23 female closed high schwa 88 774
## 1170 P39 23 female closed low schwa 180 824
## 1171 P39 23 female closed low schwa 147 796
## 1172 P39 23 female closed high I 141 690
## 1173 P39 23 female closed high I 116 727
## 1174 P39 23 female closed low I 163 666
## 1175 P39 23 female closed low I 149 692
## 1176 P39 23 female closed high Y 162 679
## 1177 P39 23 female closed high Y 148 694
## 1178 P39 23 female closed low Y 146 704
## 1179 P39 23 female closed low Y 147 695
## 1180 P5 26 male open high a 348 755
## 1181 P5 26 male open high a 312 760
## 1182 P5 26 male open low a 325 723
## 1183 P5 26 male open low a 352 799
## 1184 P5 26 male open high low_o 333 494
## 1185 P5 26 male open high low_o 328 511
## 1186 P5 26 male open low low_o 328 497
## 1187 P5 26 male open low low_o 357 467
## 1188 P5 26 male open high ou 395 422
## 1189 P5 26 male open low ou 387 407
## 1190 P5 26 male open low ou 301 385
## 1191 P5 26 male open high unround_o 441 424
## 1192 P5 26 male open high unround_o 346 434
## 1193 P5 26 male open low unround_o 316 381
## 1194 P5 26 male open low unround_o 368 436
## 1195 P5 26 male open high e 370 540
## 1196 P5 26 male open high e 263 506
## 1197 P5 26 male open low e 308 476
## 1198 P5 26 male open low e 343 434
## 1199 P5 26 male open high E 353 495
## 1200 P5 26 male open high E 295 505
## 1201 P5 26 male open low E 352 468
## 1202 P5 26 male open low E 314 433
## 1203 P5 26 male open high round_e 292 366
## 1204 P5 26 male open high round_e 302 350
## 1205 P5 26 male open low round_e 450 320
## 1206 P5 26 male open low round_e 293 370
## 1207 P5 26 male open high i 299 296
## 1208 P5 26 male open high i 273 291
## 1209 P5 26 male open low i 339 264
## 1210 P5 26 male open low i 229 287
## 1211 P5 26 male open high u 266 455
## 1212 P5 26 male open high u 355 341
## 1213 P5 26 male open low u 390 289
## 1214 P5 26 male open high y 305 314
## 1215 P5 26 male open high y 324 323
## 1216 P5 26 male open low y 390 292
## 1217 P5 26 male open low y 431 304
## 1218 P5 26 male closed high A 141 745
## 1219 P5 26 male closed high A 148 759
## 1220 P5 26 male closed low A 248 655
## 1221 P5 26 male closed low A 160 706
## 1222 P5 26 male closed high o 185 722
## 1223 P5 26 male closed high o 199 592
## 1224 P5 26 male closed low o 226 455
## 1225 P5 26 male closed low o 223 560
## 1226 P5 26 male closed high schwa 210 644
## 1227 P5 26 male closed high schwa 172 688
## 1228 P5 26 male closed low schwa 315 707
## 1229 P5 26 male closed high I 182 503
## 1230 P5 26 male closed high I 184 617
## 1231 P5 26 male closed low I 178 482
## 1232 P5 26 male closed high Y 230 590
## 1233 P5 26 male closed high Y 127 470
## 1234 P5 26 male closed low Y 148 573
## F2_midpoint F1_0.75point F2_0.75point H1.H2 f0_gap creakiness highness
## 1 1738 1064 1704 10.44 0 0 low
## 2 1573 963 1580 -1.42 0 0 low
## 3 1815 1079 1784 5.27 0 0 low
## 4 1537 1125 1809 4.57 0 0 low
## 5 866 674 965 -4.20 0 0 mid
## 6 880 442 876 -1.91 0 0 mid
## 7 939 481 971 2.32 0 0 mid
## 8 936 540 969 0.54 0 0 mid
## 9 768 467 924 0.78 0 0 high
## 10 737 295 471 0.00 0 0 high
## 11 648 340 411 6.32 0 0 high
## 12 727 441 695 -0.67 0 0 high
## 13 1075 334 641 -1.18 0 0 mid
## 14 997 417 930 -0.35 0 0 mid
## 15 1291 351 1041 2.86 0 0 mid
## 16 1253 411 1167 1.05 0 0 mid
## 17 2494 392 2423 -8.25 0 0 mid
## 18 2430 659 2476 -9.34 0 0 mid
## 19 2511 517 2397 -0.04 0 0 mid
## 20 2532 567 2511 -1.10 0 0 mid
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## 23 2484 468 2435 -1.12 0 0 mid
## 24 2502 535 2467 -0.47 2 1 mid
## 25 1938 320 1985 2.40 0 0 mid
## 26 1970 355 1925 3.47 0 0 mid
## 27 1740 379 1926 -1.84 0 0 mid
## 28 1789 337 1925 -3.45 0 0 mid
## 29 2880 265 2750 13.14 0 0 high
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## 31 2787 346 2787 6.13 0 0 high
## 32 2778 441 2785 5.01 0 0 high
## 33 858 321 626 0.18 0 0 high
## 34 797 345 966 1.08 0 0 high
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## 36 917 326 787 0.95 0 0 high
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## 40 2352 299 2303 15.23 0 0 high
## 41 1774 851 1712 -3.13 1 1 low
## 42 1902 739 1930 -1.06 0 0 low
## 43 1722 981 1698 2.51 0 0 low
## 44 1780 967 1782 -1.67 0 0 low
## 45 1001 518 1075 -11.79 0 0 high
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## 47 1143 693 1179 -0.16 1 1 high
## 48 1068 681 1134 -6.01 0 0 high
## 49 1826 868 1641 -20.36 0 0 mid
## 50 1705 977 1468 -1.43 0 0 mid
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## 52 2278 487 2209 -5.00 0 0 high
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## 57 1839 369 1731 6.17 1 1 high
## 58 1925 795 1897 -4.88 0 0 high
## 59 1908 810 1877 -3.29 0 0 high
## 60 1320 902 1312 8.71 0 0 low
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## 64 754 550 911 -15.12 0 0 mid
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## 68 762 563 873 4.01 2 1 high
## 69 533 368 569 5.18 0 0 high
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## 87 1603 491 2141 4.97 0 0 mid
## 88 2018 406 2964 8.37 1 1 high
## 89 2928 437 2990 3.27 0 0 high
## 90 2294 375 1807 7.52 0 0 high
## 91 1710 266 1941 17.66 2 1 high
## 92 539 527 754 -3.90 0 0 high
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## 104 948 507 998 -8.81 0 0 high
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## 107 996 530 1007 -11.73 1 1 high
## 108 1465 698 1470 -24.95 0 0 mid
## 109 1197 718 1489 -10.73 1 1 mid
## 110 1197 912 1328 -6.31 0 0 mid
## 111 2655 575 2596 5.18 0 0 high
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## 121 1131 658 1112 10.35 2 1 low
## 122 755 519 807 10.76 0 0 mid
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## 126 663 279 785 11.85 0 0 high
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## 141 1749 486 1716 NA 0 0 mid
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## 145 1651 310 1659 7.28 0 0 mid
## 146 2020 201 2009 2.59 0 0 high
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## 159 818 519 807 NA 0 0 high
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## 162 945 597 927 NA 2 1 high
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## 164 1349 656 1363 NA 0 0 mid
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## 167 1794 500 1785 NA 0 0 high
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## 183 606 354 771 -10.29 0 0 high
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## 203 2348 322 2269 -12.08 0 0 high
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## 212 1298 725 1342 NA 1 1 low
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## 222 1266 683 1253 NA 0 0 mid
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## 942 2036 609 1880 6.01 0 0 high
## 943 1932 712 1659 -8.68 0 0 high
## 944 1502 655 1439 5.65 0 0 high
## 945 1615 681 1568 -0.79 0 0 high
## 946 1538 642 1579 -2.79 0 0 high
## 947 1335 671 1374 2.78 0 0 high
## 948 1475 752 1149 8.29 0 0 low
## 949 1541 976 1408 -8.73 0 0 low
## 950 1621 1072 1507 -0.33 0 0 low
## 951 1332 1045 1488 -2.82 0 0 low
## 952 843 483 953 -36.36 0 0 mid
## 953 859 469 929 -21.21 0 0 mid
## 954 894 618 952 -15.69 2 1 mid
## 955 794 529 898 -6.59 2 1 mid
## 956 700 488 835 -21.43 0 0 high
## 957 705 515 761 -10.99 0 0 high
## 958 725 543 843 -19.30 0 0 high
## 959 773 532 771 -20.52 1 1 high
## 960 1365 482 1191 -26.72 0 0 mid
## 961 1566 493 1461 -23.14 0 0 mid
## 962 1474 516 1250 -11.18 0 0 mid
## 963 1426 522 1278 -8.51 0 0 mid
## 964 2335 483 2670 -18.43 0 0 mid
## 965 2311 480 2669 -23.09 0 0 mid
## 966 2546 447 2753 -14.65 0 0 mid
## 967 2574 470 2807 -12.49 0 0 mid
## 968 2384 471 2364 -32.56 0 0 mid
## 969 2367 505 2287 -18.62 3 1 mid
## 970 2330 519 2543 3.86 0 0 mid
## 971 2367 518 2362 -1.62 0 0 mid
## 972 2328 499 2072 -5.60 2 1 mid
## 973 2083 452 2021 -10.20 2 1 mid
## 974 2071 467 2448 -6.69 0 1 mid
## 975 1966 514 2126 -10.90 0 1 mid
## 976 2803 477 2853 -5.71 0 0 high
## 977 2818 501 2787 -17.14 0 0 high
## 978 2215 504 3012 -14.58 0 0 high
## 979 2673 461 2550 -3.31 0 0 high
## 980 815 274 458 -18.38 0 0 high
## 981 476 343 520 -19.42 0 0 high
## 982 932 450 628 NA 0 0 high
## 983 2199 506 2036 -13.60 0 0 high
## 984 2184 460 2194 -25.40 1 1 high
## 985 1337 585 995 -21.49 0 0 low
## 986 979 664 923 -20.73 0 0 low
## 987 1008 581 1119 -0.79 0 0 low
## 988 995 610 952 NA 0 0 high
## 989 1047 507 975 -37.97 0 0 high
## 990 978 614 1011 -0.71 0 0 high
## 991 950 626 968 -19.45 1 0 high
## 992 1529 834 1502 -27.87 0 0 mid
## 993 1183 692 1506 -23.51 0 0 mid
## 994 1599 643 1171 -9.80 0 0 mid
## 995 2299 541 2281 -8.02 1 0 high
## 996 2036 574 2153 27.98 6 0 high
## 997 2305 565 2232 -17.95 1 0 high
## 998 2354 573 2297 NA 1 0 high
## 999 1498 529 1687 27.51 0 0 high
## 1000 1845 545 1840 -12.08 1 0 high
## 1001 1652 578 1747 -2.38 0 0 high
## 1002 1252 911 1297 6.25 1 1 low
## 1003 1577 585 1109 8.19 0 0 low
## 1004 1279 1101 1385 12.86 1 1 low
## 1005 1287 949 1300 10.28 1 1 low
## 1006 829 415 872 3.15 0 0 mid
## 1007 836 498 866 6.89 1 0 mid
## 1008 863 515 812 18.83 0 0 mid
## 1009 831 269 823 13.07 0 0 mid
## 1010 605 274 700 3.30 1 1 high
## 1011 518 276 589 2.64 1 0 high
## 1012 735 236 785 14.14 0 0 high
## 1013 893 338 711 17.31 0 0 high
## 1014 1394 298 1330 8.45 0 0 mid
## 1015 1482 270 1419 4.60 0 0 mid
## 1016 1337 314 1336 18.22 0 0 mid
## 1017 1392 310 1395 12.35 0 1 mid
## 1018 2349 225 2316 4.28 0 0 mid
## 1019 2169 218 2239 6.42 0 0 mid
## 1020 2239 247 2432 16.76 0 0 mid
## 1021 2361 248 2420 13.79 0 0 mid
## 1022 2100 207 1994 2.99 0 0 mid
## 1023 2098 237 2037 2.03 0 0 mid
## 1024 2102 301 1959 17.00 1 1 mid
## 1025 2152 477 2052 11.04 0 0 mid
## 1026 1641 272 1617 2.37 0 0 mid
## 1027 1644 227 1670 -3.58 0 0 mid
## 1028 1548 270 1549 16.47 0 0 mid
## 1029 1548 318 1555 7.94 0 0 mid
## 1030 2649 256 2580 1.90 0 0 high
## 1031 2375 258 2334 2.37 2 0 high
## 1032 2446 224 2294 14.40 0 0 high
## 1033 2551 278 2418 6.67 1 0 high
## 1034 868 268 822 5.17 0 0 high
## 1035 887 270 1125 2.75 0 0 high
## 1036 860 325 817 14.85 1 1 high
## 1037 1224 313 1105 10.47 1 1 high
## 1038 1877 209 1777 4.32 0 0 high
## 1039 1864 283 1812 4.98 0 0 high
## 1040 1909 300 1774 13.81 1 0 high
## 1041 1813 297 1788 8.31 0 0 high
## 1042 1496 879 1463 3.09 0 0 low
## 1043 1544 836 1464 5.89 0 0 low
## 1044 1384 711 1334 21.25 0 0 low
## 1045 1363 909 1373 10.80 0 0 low
## 1046 792 319 801 0.59 0 0 high
## 1047 858 339 866 3.74 0 0 high
## 1048 908 351 930 7.40 0 0 high
## 1049 866 118 873 10.62 0 0 high
## 1050 1393 794 1361 5.42 0 0 mid
## 1051 1448 668 1404 4.98 0 0 mid
## 1052 1334 1253 1366 21.96 0 0 mid
## 1053 1843 418 1791 0.63 0 0 high
## 1054 2036 359 1852 -1.15 0 0 high
## 1055 1998 301 1795 5.05 0 0 high
## 1056 2113 600 2003 5.86 0 0 high
## 1057 1355 380 1326 4.47 1 1 high
## 1058 1402 541 1351 3.32 0 0 high
## 1059 1457 426 1345 11.45 0 0 high
## 1060 1355 660 1330 7.90 0 0 high
## 1061 1536 1089 1587 5.34 0 0 low
## 1062 1577 1104 1379 5.86 0 0 low
## 1063 1467 1196 1564 7.41 0 0 low
## 1064 1590 1033 1609 5.05 0 0 low
## 1065 841 653 869 -6.10 0 0 mid
## 1066 843 576 896 -0.53 0 0 mid
## 1067 818 593 889 -3.26 0 0 mid
## 1068 778 602 927 -1.61 0 0 mid
## 1069 610 390 744 -0.04 0 0 high
## 1070 596 396 644 3.82 0 0 high
## 1071 572 363 589 -2.75 0 0 high
## 1072 564 422 660 -3.28 0 0 high
## 1073 1297 498 1195 -0.85 0 0 mid
## 1074 1310 475 1199 1.76 0 0 mid
## 1075 1356 516 1277 1.86 1 1 mid
## 1076 1296 474 1271 2.62 0 0 mid
## 1077 2278 469 2390 -0.53 0 0 mid
## 1078 2392 445 2612 1.84 0 1 mid
## 1079 1976 459 2501 -1.30 0 1 mid
## 1080 2313 465 2501 1.06 0 1 mid
## 1081 2281 628 2229 -1.78 0 0 mid
## 1082 2279 685 2282 -4.51 0 1 mid
## 1083 2336 589 2326 -4.56 0 1 mid
## 1084 2325 583 2330 -1.11 0 0 mid
## 1085 1699 397 1773 -3.11 0 0 mid
## 1086 1719 359 1755 -0.47 0 0 mid
## 1087 1738 368 1773 -9.24 0 0 mid
## 1088 1603 371 1664 -1.89 0 0 mid
## 1089 2743 357 2754 5.83 0 0 high
## 1090 2841 348 2822 -0.25 0 0 high
## 1091 2802 308 2760 -1.86 2 1 high
## 1092 2729 304 2684 -2.75 0 0 high
## 1093 777 441 724 0.88 0 0 high
## 1094 746 392 726 1.16 1 0 high
## 1095 816 503 795 -1.09 0 0 high
## 1096 745 388 739 0.17 0 0 high
## 1097 2044 352 2022 -0.39 0 0 high
## 1098 2118 374 1989 -1.91 0 0 high
## 1099 2091 400 2025 1.37 2 1 high
## 1100 1995 353 2026 -3.53 0 0 high
## 1101 1634 957 1649 4.41 0 0 low
## 1102 1624 1009 1618 3.63 0 0 low
## 1103 1644 1139 1707 -1.03 1 1 low
## 1104 1606 1029 1644 1.34 0 0 low
## 1105 847 685 951 -7.38 0 0 high
## 1106 820 662 953 -3.42 0 0 high
## 1107 857 693 954 -5.47 0 0 high
## 1108 937 758 962 -7.22 0 0 high
## 1109 1616 946 1551 6.04 0 0 mid
## 1110 1545 928 1488 4.82 0 0 mid
## 1111 1604 962 1684 -2.00 0 1 mid
## 1112 2200 718 2026 -1.53 0 0 high
## 1113 2377 618 2199 -3.56 0 0 high
## 1114 2185 689 2135 -6.99 0 0 high
## 1115 2270 740 2166 -6.06 0 0 high
## 1116 1794 728 1917 -1.77 0 0 high
## 1117 1924 635 1927 -6.58 0 0 high
## 1118 2033 683 1846 -4.71 0 0 high
## 1119 2137 742 2090 -0.83 0 0 high
## 1120 1542 914 1605 17.98 0 0 low
## 1121 1391 942 1414 8.00 1 1 low
## 1122 1410 1025 1469 7.57 1 1 low
## 1123 1424 1141 1475 7.44 1 1 low
## 1124 882 519 617 -2.23 0 0 mid
## 1125 864 655 911 -6.01 0 0 mid
## 1126 748 481 527 3.68 0 1 mid
## 1127 987 717 993 -4.55 0 1 mid
## 1128 778 476 743 4.52 0 0 high
## 1129 894 516 688 2.35 1 1 high
## 1130 666 430 838 9.83 1 0 high
## 1131 791 426 820 12.19 0 0 high
## 1132 1242 372 1180 -0.56 0 0 mid
## 1133 1433 418 1411 -1.09 1 1 mid
## 1134 1154 392 1285 12.86 1 1 mid
## 1135 1256 431 1212 -2.36 0 1 mid
## 1136 2467 354 2574 NA 1 1 mid
## 1137 2531 404 2590 20.02 0 0 mid
## 1138 2502 402 2551 4.05 5 1 mid
## 1139 2526 305 2590 2.17 2 1 mid
## 1140 2366 688 2322 -2.45 0 0 mid
## 1141 2382 565 2270 8.60 1 1 mid
## 1142 2410 667 2448 -4.97 0 0 mid
## 1143 2421 554 2351 -4.10 0 0 mid
## 1144 1544 458 1558 -5.21 0 0 mid
## 1145 1721 450 1765 11.80 0 0 mid
## 1146 1818 374 1735 -4.82 0 0 mid
## 1147 1690 483 1688 -9.99 1 1 mid
## 1148 2833 303 2840 6.77 0 0 high
## 1149 2752 330 2744 6.45 0 0 high
## 1150 2651 399 2603 13.38 1 0 high
## 1151 2832 401 2687 8.82 0 0 high
## 1152 999 509 962 -6.64 0 0 high
## 1153 921 493 865 -4.11 0 0 high
## 1154 878 437 911 -6.92 0 0 high
## 1155 878 473 921 -3.37 0 0 high
## 1156 1923 396 1951 1.05 0 0 high
## 1157 1867 422 1928 -2.85 0 0 high
## 1158 2010 318 1991 4.71 0 0 high
## 1159 1936 368 1933 3.95 2 1 high
## 1160 1507 816 1503 -4.18 0 0 low
## 1161 1520 827 1506 -4.14 0 0 low
## 1162 1429 767 1484 0.53 2 1 low
## 1163 1410 745 1431 -0.82 0 0 low
## 1164 1070 768 1036 -12.48 0 0 high
## 1165 1086 881 1177 9.11 1 1 high
## 1166 1195 824 1167 -0.51 0 0 high
## 1167 1157 803 1217 -2.01 1 0 high
## 1168 1308 784 1403 -1.20 0 0 mid
## 1169 1613 712 1583 -0.96 0 0 mid
## 1170 1511 820 1515 1.65 0 0 mid
## 1171 1664 722 1514 0.13 0 0 mid
## 1172 2233 780 2140 -4.69 0 0 high
## 1173 2174 766 2090 -2.65 0 0 high
## 1174 2208 694 2099 -3.91 0 0 high
## 1175 2301 784 2074 -2.47 0 0 high
## 1176 1442 753 1404 -3.11 0 0 high
## 1177 1458 724 1412 -1.87 0 0 high
## 1178 1381 767 1417 -1.17 0 0 high
## 1179 1384 743 1366 1.06 0 0 high
## 1180 1169 754 1189 8.22 0 0 low
## 1181 1154 746 1166 2.32 1 1 low
## 1182 1199 703 1175 0.34 0 0 low
## 1183 1147 754 1102 1.29 0 0 low
## 1184 615 517 690 -5.30 0 0 mid
## 1185 665 562 652 -3.05 1 0 mid
## 1186 713 548 809 -3.73 0 0 mid
## 1187 584 531 725 -4.11 0 1 mid
## 1188 692 338 638 0.10 0 0 high
## 1189 759 324 639 -3.39 0 0 high
## 1190 749 324 613 -2.57 0 0 high
## 1191 1341 370 1256 -3.26 6 1 mid
## 1192 1408 370 1382 -2.83 0 0 mid
## 1193 1299 394 1365 -3.57 0 0 mid
## 1194 1277 369 1279 -1.72 1 1 mid
## 1195 1982 520 1892 -3.77 0 0 mid
## 1196 1924 510 1943 -1.38 0 0 mid
## 1197 2361 471 2007 -4.37 1 0 mid
## 1198 2025 474 1960 -2.63 0 0 mid
## 1199 2176 519 1929 -3.38 0 0 mid
## 1200 1958 500 1976 -4.23 0 0 mid
## 1201 2001 538 1969 -2.41 0 0 mid
## 1202 1966 498 1887 -1.18 0 0 mid
## 1203 1396 315 1409 -3.29 0 0 mid
## 1204 1387 312 1361 -7.31 0 0 mid
## 1205 1398 380 1343 -6.80 0 0 mid
## 1206 1385 316 1323 -4.50 1 0 mid
## 1207 2307 320 3151 -5.09 0 0 high
## 1208 2435 315 2443 -5.92 0 0 high
## 1209 2328 307 2460 -2.06 0 0 high
## 1210 2854 276 2422 -2.16 0 0 high
## 1211 503 323 677 -4.12 0 0 high
## 1212 861 364 734 -5.04 0 0 high
## 1213 531 167 490 -3.37 1 1 high
## 1214 1832 326 1775 -3.30 0 0 high
## 1215 1989 310 1776 2.61 1 0 high
## 1216 1831 324 1700 2.84 0 0 high
## 1217 1735 329 1635 -5.16 0 0 high
## 1218 1242 726 1205 -3.34 0 0 low
## 1219 1249 754 1241 -6.30 0 0 low
## 1220 1201 739 1216 -5.19 0 0 low
## 1221 1203 720 1206 2.23 0 0 low
## 1222 1076 737 1064 -1.56 2 0 high
## 1223 1153 683 1106 -1.95 0 0 high
## 1224 694 557 874 -4.34 1 1 high
## 1225 1097 640 1155 0.10 0 0 high
## 1226 1220 703 1215 -1.68 0 0 mid
## 1227 1353 694 1109 -1.93 0 0 mid
## 1228 1135 751 1165 -1.90 1 1 mid
## 1229 1949 664 1651 -5.83 0 0 high
## 1230 1717 721 1492 -4.99 1 1 high
## 1231 1697 685 1392 -9.44 0 0 high
## 1232 1061 646 1089 -5.36 0 0 high
## 1233 1167 585 1440 -2.78 0 0 high
## 1234 1135 626 1176 -5.09 0 0 high
## backness spectrum_difference
## 1 mid 0.011730205
## 2 mid 0.017985612
## 3 mid 0.013609467
## 4 mid 0.013953488
## 5 back 0.007940447
## 6 back 0.014331210
## 7 back 0.010182768
## 8 back 0.013554217
## 9 back 0.008188586
## 10 back 0.011209440
## 11 back 0.011911357
## 12 back 0.010649351
## 13 mid 0.007476636
## 14 mid 0.009703504
## 15 mid 0.013981763
## 16 mid 0.013256484
## 17 front 0.014333333
## 18 front 0.012202381
## 19 front 0.012188366
## 20 front 0.016352201
## 21 front 0.009308511
## 22 front 0.013125000
## 23 front 0.014457831
## 24 front 0.011845730
## 25 front 0.008585859
## 26 front 0.010140845
## 27 front 0.017905405
## 28 front 0.018088737
## 29 front 0.007226107
## 30 front 0.009066667
## 31 front 0.012256267
## 32 front 0.012680115
## 33 back 0.010946746
## 34 back 0.007960199
## 35 back 0.009367089
## 36 back 0.017532468
## 37 front 0.010734463
## 38 front 0.016896552
## 39 front 0.015923567
## 40 front 0.023371648
## 41 mid 0.064661654
## 42 mid 0.045679012
## 43 mid 0.044670051
## 44 mid 0.075200000
## 45 back 0.049056604
## 46 back 0.026720648
## 47 back 0.025200000
## 48 back 0.038118812
## 49 mid 0.078977273
## 50 mid 0.050632911
## 51 mid 0.034841629
## 52 front 0.068750000
## 53 front 0.057042254
## 54 front 0.026748971
## 55 front 0.018815331
## 56 front 0.053020134
## 57 front 0.049685535
## 58 front 0.031081081
## 59 front 0.020664207
## 60 mid 0.007812500
## 61 mid 0.009497207
## 62 mid 0.006034483
## 63 mid 0.006073753
## 64 back 0.004000000
## 65 back 0.007142857
## 66 back 0.006833713
## 67 back 0.007940447
## 68 back 0.006264501
## 69 back 0.005309735
## 70 back 0.006787330
## 71 back 0.008542714
## 72 mid 0.012218650
## 73 mid 0.008196721
## 74 mid 0.008808290
## 75 mid 0.010787172
## 76 front 0.008222812
## 77 front 0.012380952
## 78 front 0.006081081
## 79 front 0.007750000
## 80 front 0.009593023
## 81 front 0.004483431
## 82 front 0.008244681
## 83 front 0.007075472
## 84 front 0.007070707
## 85 front 0.009295775
## 86 front 0.007345972
## 87 front 0.009115282
## 88 front 0.004637097
## 89 front 0.005518764
## 90 front 0.007416268
## 91 front 0.005220884
## 92 back 0.006419753
## 93 back 0.008659218
## 94 back 0.023245614
## 95 back 0.008800000
## 96 front 0.009497207
## 97 front 0.008516484
## 98 front 0.009473684
## 99 front 0.012804878
## 100 mid 0.105102041
## 101 mid 0.064843750
## 102 mid 0.017777778
## 103 mid 0.024553571
## 104 back 0.062222222
## 105 back 0.137931034
## 106 back 0.038172043
## 107 back 0.031794872
## 108 mid 0.208823529
## 109 mid 0.048666667
## 110 mid 0.018631179
## 111 front 0.069166667
## 112 front 0.051724138
## 113 front 0.035638298
## 114 front 0.027649770
## 115 front 0.049668874
## 116 front 0.048965517
## 117 front 0.017328520
## 118 front 0.067460317
## 119 mid 0.053679654
## 120 mid 0.073300971
## 121 mid 0.039361702
## 122 back 0.039917695
## 123 back 0.033333333
## 124 back 0.042798354
## 125 back 0.034657040
## 126 back 0.029078014
## 127 back 0.028260870
## 128 back 0.063228700
## 129 back 0.055508475
## 130 mid 0.062441315
## 131 mid 0.057487923
## 132 mid 0.055263158
## 133 mid 0.032179931
## 134 front 0.042424242
## 135 front 0.041224490
## 136 front 0.048760331
## 137 front 0.038745387
## 138 front 0.037401575
## 139 front 0.040740741
## 140 front 0.068137255
## 141 front 0.000000000
## 142 front 0.051674641
## 143 front 0.033712121
## 144 front 0.044855967
## 145 front 0.041101695
## 146 front 0.036121673
## 147 front 0.060576923
## 148 front 0.050207469
## 149 front 0.062616822
## 150 back 0.048309179
## 151 back 0.049327354
## 152 back 0.067149758
## 153 front 0.087244898
## 154 front 0.040659341
## 155 mid 0.137096774
## 156 mid 0.284090909
## 157 mid 0.185483871
## 158 mid 0.159420290
## 159 back NA
## 160 back 0.077319588
## 161 back 0.075568182
## 162 back NA
## 163 mid 0.137593985
## 164 mid NA
## 165 mid 0.164233577
## 166 mid 0.185600000
## 167 front NA
## 168 front 0.171304348
## 169 front 0.150354610
## 170 front 0.122000000
## 171 front 0.209821429
## 172 front 0.141958042
## 173 front 0.189075630
## 174 front 0.201834862
## 175 mid 0.018108108
## 176 mid 0.024489796
## 177 mid 0.015839243
## 178 mid 0.029585799
## 179 back 0.017021277
## 180 back 0.018617021
## 181 back 0.017587940
## 182 back 0.018518519
## 183 back 0.016755319
## 184 back 0.017528736
## 185 back 0.017171717
## 186 back 0.015158924
## 187 mid 0.015482234
## 188 mid 0.013054187
## 189 mid 0.013071895
## 190 mid 0.029260450
## 191 front 0.028145695
## 192 front 0.017994100
## 193 front 0.017401961
## 194 front 0.023323615
## 195 front 0.023853211
## 196 front 0.023219814
## 197 front 0.013921114
## 198 front 0.027848101
## 199 front 0.018639053
## 200 front 0.021069182
## 201 front 0.016153846
## 202 front 0.018233618
## 203 front 0.021183801
## 204 front 0.018911175
## 205 front 0.026510067
## 206 front 0.016790123
## 207 back 0.013888889
## 208 back 0.018604651
## 209 back 0.027760252
## 210 front 0.021929825
## 211 front 0.031071429
## 212 mid NA
## 213 mid 0.163063063
## 214 mid 0.174626866
## 215 mid 0.101863354
## 216 back 0.107913669
## 217 back 0.168932039
## 218 back 0.242857143
## 219 back 0.146969697
## 220 mid 0.294520548
## 221 mid 0.262337662
## 222 mid NA
## 223 front 0.182222222
## 224 front 0.112765957
## 225 front 0.141891892
## 226 front NA
## 227 front 0.232380952
## 228 front 0.169565217
## 229 mid 0.013953488
## 230 mid 0.003435805
## 231 mid 0.004761905
## 232 mid 0.006666667
## 233 back 0.005298013
## 234 back 0.005895197
## 235 back 0.006756757
## 236 back 0.016878981
## 237 back 0.007106599
## 238 back 0.002759740
## 239 back 0.009090909
## 240 mid 0.007591623
## 241 mid 0.007360406
## 242 mid 0.007111111
## 243 mid 0.012574850
## 244 front 0.007073171
## 245 front 0.008152174
## 246 front 0.006611570
## 247 front 0.006451613
## 248 front 0.008021390
## 249 front 0.010483871
## 250 front 0.009487179
## 251 front 0.006352941
## 252 front 0.008060453
## 253 front 0.007017544
## 254 front 0.015942029
## 255 front 0.009593023
## 256 front 0.009850746
## 257 front 0.006339468
## 258 front 0.005893910
## 259 back 0.012101911
## 260 back 0.007575758
## 261 back 0.013855422
## 262 front 0.005042017
## 263 front 0.019607843
## 264 front 0.011834320
## 265 mid 0.101941748
## 266 mid 0.083870968
## 267 mid 0.053488372
## 268 mid 0.073134328
## 269 back 0.041250000
## 270 back 0.115000000
## 271 back 0.049112426
## 272 back 0.047368421
## 273 mid 0.092920354
## 274 mid 0.206493506
## 275 mid 0.114159292
## 276 front 0.085840708
## 277 front 0.076724138
## 278 front 0.165979381
## 279 front 0.082575758
## 280 front 0.070542636
## 281 front 0.052173913
## 282 front 0.033854167
## 283 mid 0.020626632
## 284 mid 0.008579088
## 285 back 0.008831169
## 286 back 0.006561086
## 287 mid 0.011173184
## 288 mid 0.007920792
## 289 mid 0.016271186
## 290 mid 0.014939024
## 291 front 0.006359649
## 292 front 0.010540541
## 293 front 0.009207161
## 294 front 0.005405405
## 295 front 0.011239193
## 296 front 0.009340659
## 297 front 0.008866995
## 298 front 0.010888252
## 299 front 0.009549072
## 300 front 0.013761468
## 301 front 0.013213213
## 302 mid 0.077678571
## 303 mid 0.057042254
## 304 back 0.100961538
## 305 back 0.100980392
## 306 mid 0.025652174
## 307 mid 0.154639175
## 308 mid 0.034158416
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## 1035 back 0.021854305
## 1036 back 0.024620061
## 1037 back 0.012616822
## 1038 front 0.026845638
## 1039 front 0.020180723
## 1040 front 0.015841584
## 1041 front 0.021944444
## 1042 mid 0.245652174
## 1043 mid 0.207920792
## 1044 mid 0.083695652
## 1045 mid 0.087005650
## 1046 back 0.159633028
## 1047 back 0.174782609
## 1048 back 0.107006369
## 1049 back 0.107500000
## 1050 mid 0.194392523
## 1051 mid 0.243956044
## 1052 mid 0.089349112
## 1053 front 0.121739130
## 1054 front 0.152678571
## 1055 front 0.090804598
## 1056 front 0.104458599
## 1057 front 0.083832335
## 1058 front 0.147008547
## 1059 front 0.088095238
## 1060 front 0.065853659
## 1061 mid 0.016975309
## 1062 mid 0.019078947
## 1063 mid 0.016531165
## 1064 mid 0.026517572
## 1065 back 0.010576923
## 1066 back 0.009579439
## 1067 back 0.012592593
## 1068 back 0.017307692
## 1069 back 0.010144928
## 1070 back 0.016871166
## 1071 back 0.028014184
## 1072 back 0.024193548
## 1073 mid 0.014369501
## 1074 mid 0.009555556
## 1075 mid 0.018571429
## 1076 mid 0.012880562
## 1077 front 0.012500000
## 1078 front 0.009170306
## 1079 front 0.009956710
## 1080 front 0.011512415
## 1081 front 0.008883827
## 1082 front 0.007727273
## 1083 front 0.011764706
## 1084 front 0.015064935
## 1085 front 0.005033557
## 1086 front 0.011002445
## 1087 front 0.010561798
## 1088 front 0.019512195
## 1089 front 0.011968085
## 1090 front 0.009767442
## 1091 front 0.010729614
## 1092 front 0.009543568
## 1093 back 0.011392405
## 1094 back 0.011369509
## 1095 back 0.036437247
## 1096 back 0.008407080
## 1097 front 0.010268949
## 1098 front 0.012106538
## 1099 front 0.013915094
## 1100 front 0.008924949
## 1101 mid 0.163725490
## 1102 mid 0.184210526
## 1103 mid 0.080346821
## 1104 mid 0.067741935
## 1105 back 0.097580645
## 1106 back 0.133027523
## 1107 back 0.128828829
## 1108 back 0.077464789
## 1109 mid 0.148181818
## 1110 mid 0.175000000
## 1111 mid 0.080239521
## 1112 front 0.097014925
## 1113 front 0.101562500
## 1114 front 0.096000000
## 1115 front 0.061052632
## 1116 front 0.073333333
## 1117 front 0.274698795
## 1118 front 0.099218750
## 1119 front 0.099333333
## 1120 mid 0.009245742
## 1121 mid 0.058303887
## 1122 mid 0.028296703
## 1123 mid 0.021221865
## 1124 back 0.012564103
## 1125 back 0.016510903
## 1126 back 0.012771739
## 1127 back 0.015963855
## 1128 back 0.011019284
## 1129 back 0.011780822
## 1130 back 0.013440860
## 1131 back 0.013782991
## 1132 mid 0.024324324
## 1133 mid 0.007659574
## 1134 mid 0.025324675
## 1135 mid 0.011374408
## 1136 front 0.000000000
## 1137 front 0.021409922
## 1138 front 0.019626168
## 1139 front 0.011176471
## 1140 front 0.013353116
## 1141 front 0.013573407
## 1142 front 0.026449275
## 1143 front 0.024647887
## 1144 front 0.014556962
## 1145 front 0.029042904
## 1146 front 0.013994169
## 1147 front 0.021262458
## 1148 front 0.011021505
## 1149 front 0.008333333
## 1150 front 0.021283784
## 1151 front 0.016447368
## 1152 back 0.023076923
## 1153 back 0.014153846
## 1154 back 0.018556701
## 1155 back 0.015361446
## 1156 front 0.013235294
## 1157 front 0.012195122
## 1158 front 0.015300546
## 1159 front 0.009768638
## 1160 mid 0.160674157
## 1161 mid 0.114953271
## 1162 mid 0.101503759
## 1163 mid 0.064880952
## 1164 back 0.098275862
## 1165 back 0.031578947
## 1166 back 0.093150685
## 1167 back 0.107936508
## 1168 mid 0.035849057
## 1169 mid 0.169318182
## 1170 mid 0.061111111
## 1171 mid 0.099319728
## 1172 front 0.065957447
## 1173 front 0.094827586
## 1174 front 0.067484663
## 1175 front 0.084563758
## 1176 front 0.069135802
## 1177 front 0.085810811
## 1178 front 0.091780822
## 1179 front 0.088435374
## 1180 mid 0.015517241
## 1181 mid 0.020192308
## 1182 mid 0.022153846
## 1183 mid 0.021306818
## 1184 back 0.019519520
## 1185 back 0.021951220
## 1186 back 0.019817073
## 1187 back 0.019607843
## 1188 back 0.015189873
## 1189 back 0.015762274
## 1190 back 0.026910299
## 1191 mid 0.012018141
## 1192 mid 0.017052023
## 1193 mid 0.026898734
## 1194 mid 0.018206522
## 1195 front 0.014324324
## 1196 front 0.033460076
## 1197 front 0.023051948
## 1198 front 0.020991254
## 1199 front 0.018413598
## 1200 front 0.023728814
## 1201 front 0.019886364
## 1202 front 0.024522293
## 1203 front 0.027739726
## 1204 front 0.023841060
## 1205 front 0.011333333
## 1206 front 0.027986348
## 1207 front 0.023076923
## 1208 front 0.029304029
## 1209 front 0.020943953
## 1210 front 0.050655022
## 1211 back 0.034586466
## 1212 back 0.015492958
## 1213 back 0.016410256
## 1214 front 0.023606557
## 1215 front 0.022530864
## 1216 front 0.016153846
## 1217 front 0.013689095
## 1218 mid 0.086524823
## 1219 mid 0.075675676
## 1220 mid 0.044354839
## 1221 mid 0.066250000
## 1222 back 0.057837838
## 1223 back 0.062311558
## 1224 back 0.049557522
## 1225 back 0.050224215
## 1226 mid 0.054761905
## 1227 mid 0.078488372
## 1228 mid 0.024126984
## 1229 front 0.046703297
## 1230 front 0.048913043
## 1231 front 0.082584270
## 1232 front 0.040869565
## 1233 front 0.122834646
## 1234 front 0.095945946
levels(yng$vowel)
## [1] "a" "A" "e" "E" "i" "I"
## [7] "low_o" "o" "ou" "round_e" "schwa" "u"
## [13] "unround_o" "y" "Y"
is.integer(yng$Duration)
## [1] TRUE
result_duration <- data.frame(vowel = character(),
mean = numeric(),
sd = numeric(),
stringsAsFactors = FALSE)
for (vowel in levels(yng$vowel)){
dt.duration <- yng[yng$vowel == vowel,"Duration"]
vowel_mean <- round(mean(dt.duration))
vowel_sd <- round(sd(dt.duration))
new_row <- data.frame(vowel = vowel,
mean = vowel_mean,
sd = vowel_sd,
stringsAsFactors = FALSE)
# Append the new row to the result data frame
result_duration <- rbind(result_duration, new_row)
}
print(result_duration)
## vowel mean sd
## 1 a 348 75
## 2 A 144 45
## 3 e 354 72
## 4 E 349 68
## 5 i 353 78
## 6 I 146 45
## 7 low_o 364 68
## 8 o 146 48
## 9 ou 355 69
## 10 round_e 346 64
## 11 schwa 142 54
## 12 u 332 74
## 13 unround_o 354 65
## 14 y 344 63
## 15 Y 145 45
for (vowel in levels(yng$vowel)){
dt.F1midpoint <- yng[yng$vowel == vowel,"F1_midpoint"]
vowel_mean <- round(mean(dt.duration))
vowel_sd <- round(sd(dt.duration))
new_row <- data.frame(vowel = vowel,
mean = vowel_mean,
sd = vowel_sd,
stringsAsFactors = FALSE)
# Append the new row to the result data frame
result_duration <- rbind(result_duration, new_row)
}
print(result_duration)
## vowel mean sd
## 1 a 348 75
## 2 A 144 45
## 3 e 354 72
## 4 E 349 68
## 5 i 353 78
## 6 I 146 45
## 7 low_o 364 68
## 8 o 146 48
## 9 ou 355 69
## 10 round_e 346 64
## 11 schwa 142 54
## 12 u 332 74
## 13 unround_o 354 65
## 14 y 344 63
## 15 Y 145 45
## 16 a 145 45
## 17 A 145 45
## 18 e 145 45
## 19 E 145 45
## 20 i 145 45
## 21 I 145 45
## 22 low_o 145 45
## 23 o 145 45
## 24 ou 145 45
## 25 round_e 145 45
## 26 schwa 145 45
## 27 u 145 45
## 28 unround_o 145 45
## 29 y 145 45
## 30 Y 145 45
result_formant <- data.frame(vowel = character(),
mean.female_f1_50 = numeric(), mean.female_f2_50 = numeric(),
mean.female_f1_75 = numeric(), mean.female_f2_75 = numeric(),
mean.male_f1_50 = numeric(), mean.male_f2_50 = numeric(),
mean.male_f1_75 = numeric(), mean.male_f2_75 = numeric(),
sd.female_f1_50 = numeric(), sd.female_f2_50 = numeric(),
sd.female_f1_75 = numeric(), sd.female_f2_75 = numeric(),
sd.male_f1_50 = numeric(), sd.male_f2_50 = numeric(),
sd.male_f1_75 = numeric(), sd.male_f2_75 = numeric(),
stringsAsFactors = FALSE)
for (vowel in levels(yng$vowel)){
dt.male_f1_50 <- yng[yng$vowel == vowel & yng$gender == "male","F1_midpoint"]
dt.male_f2_50 <- yng[yng$vowel == vowel & yng$gender == "male","F2_midpoint"]
dt.male_f1_75 <- yng[yng$vowel == vowel & yng$gender == "male","F1_0.75point"]
dt.male_f2_75 <- yng[yng$vowel == vowel & yng$gender == "male","F2_0.75point"]
dt.female_f1_50 <- yng[yng$vowel == vowel & yng$gender == "female","F1_midpoint"]
dt.female_f2_50 <- yng[yng$vowel == vowel & yng$gender == "female","F2_midpoint"]
dt.female_f1_75 <- yng[yng$vowel == vowel & yng$gender == "female","F1_0.75point"]
dt.female_f2_75 <- yng[yng$vowel == vowel & yng$gender == "female","F2_0.75point"]
male_f1_50_mean <- round(mean(dt.male_f1_50))
male_f1.50_sd <- round(sd(dt.male_f1_50))
male_f2_50_mean <- round(mean(dt.male_f2_50))
male_f2.50_sd <- round(sd(dt.male_f2_50))
male_f1_75_mean <- round(mean(dt.male_f1_75))
male_f1.75_sd <- round(sd(dt.male_f1_75))
male_f2_75_mean <- round(mean(dt.male_f2_75))
male_f2.75_sd <- round(sd(dt.male_f2_75))
female_f1_50_mean <- round(mean(dt.female_f1_50))
female_f1.50_sd <- round(sd(dt.female_f1_50))
female_f2_50_mean <- round(mean(dt.female_f2_50))
female_f2.50_sd <- round(sd(dt.female_f2_50))
female_f1_75_mean <- round(mean(dt.female_f1_75))
female_f1.75_sd <- round(sd(dt.female_f1_75))
female_f2_75_mean <- round(mean(dt.female_f2_75))
female_f2.75_sd <- round(sd(dt.female_f2_75))
new_row <- data.frame(vowel = vowel,
mean.female_f1_50 = female_f1_50_mean, mean.female_f2_50 = female_f2_50_mean,
mean.female_f1_75 = female_f1_75_mean, mean.female_f2_75 = female_f2_75_mean,
mean.male_f1_50 = male_f1_50_mean, mean.male_f2_50 = male_f2_50_mean,
mean.male_f1_75 = male_f1_75_mean, mean.male_f2_75 = male_f2_75_mean,
sd.female_f1_50 = female_f1.50_sd, sd.female_f2_50 = female_f2.50_sd,
sd.female_f1_75 = female_f1.75_sd, sd.female_f2_75 = female_f2.75_sd,
sd.male_f1_50 = male_f1.50_sd, sd.male_f2_50 = male_f2.50_sd,
sd.male_f1_75 = male_f1.75_sd, sd.male_f2_75 = male_f2.75_sd,
stringsAsFactors = FALSE)
# Append the new row to the result data frame
result_formant <- rbind(result_formant, new_row)
}
print(result_formant)
## vowel mean.female_f1_50 mean.female_f2_50 mean.female_f1_75
## 1 a 1012 1475 959
## 2 A 841 1524 860
## 3 e 482 2344 458
## 4 E 557 2274 573
## 5 i 354 2575 366
## 6 I 583 2303 626
## 7 low_o 567 853 580
## 8 o 626 1008 668
## 9 ou 415 720 446
## 10 round_e 411 1856 432
## 11 schwa 773 1572 770
## 12 u 394 834 402
## 13 unround_o 487 1338 460
## 14 y 378 2174 395
## 15 Y 611 1734 666
## mean.female_f2_75 mean.male_f1_50 mean.male_f2_50 mean.male_f1_75
## 1 1441 901 1305 859
## 2 1523 787 1409 780
## 3 2477 418 2132 365
## 4 2251 485 2030 507
## 5 2579 273 2321 284
## 6 2119 464 2027 537
## 7 886 496 775 521
## 8 1054 514 862 537
## 9 742 358 691 347
## 10 1891 336 1625 343
## 11 1550 706 1383 715
## 12 811 343 796 331
## 13 1300 433 1337 384
## 14 2122 300 1893 307
## 15 1712 512 1592 560
## mean.male_f2_75 sd.female_f1_50 sd.female_f2_50 sd.female_f1_75
## 1 1287 131 180 143
## 2 1402 115 235 189
## 3 2212 54 322 70
## 4 2016 58 295 75
## 5 2330 70 425 75
## 6 1936 77 180 99
## 7 806 83 88 85
## 8 899 83 89 101
## 9 708 78 113 86
## 10 1633 42 215 52
## 11 1375 144 193 126
## 12 787 68 198 81
## 13 1260 47 194 55
## 14 1832 65 225 69
## 15 1586 95 199 107
## sd.female_f2_75 sd.male_f1_50 sd.male_f2_50 sd.male_f1_75 sd.male_f2_75
## 1 174 146 170 205 169
## 2 230 116 146 155 162
## 3 401 84 161 85 240
## 4 251 111 148 112 150
## 5 424 45 297 51 298
## 6 242 86 185 114 214
## 7 116 80 91 90 74
## 8 109 111 115 145 109
## 9 137 57 112 65 105
## 10 256 44 139 45 162
## 11 163 136 150 161 168
## 12 184 63 146 71 164
## 13 214 99 133 56 115
## 14 204 57 129 55 131
## 15 189 94 223 111 210
library(lmerTest)
## Loading required package: lme4
## Loading required package: Matrix
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
contrast.syllable <- cbind(c(-0.5,+0.5))
colnames(contrast.syllable) <- c("-closed+open")
contrasts(yng$syllable) <- contrast.syllable
contrasts(yng$syllable)
## -closed+open
## closed -0.5
## open 0.5
contrast.gender <- cbind(c(+0.5,-0.5))
colnames(contrast.gender) <- c("+f-m")
contrasts(yng$gender) <- contrast.gender
contrasts(yng$gender)
## +f-m
## female 0.5
## male -0.5
contrast.register <- cbind(c(-0.5,+0.5))
colnames(contrast.register) <- c("-h+l")
contrasts(yng$register) <- contrast.register
contrasts(yng$register)
## -h+l
## high -0.5
## low 0.5
contrast.highness <- cbind(c(1/3,1/3,-2/3), c(-0.5,0.5,0)) #high,low,mid
colnames(contrast.highness) <- c("+HL-M","-H+L")
contrasts(yng$highness) <- contrast.highness
contrasts(yng$highness)
## +HL-M -H+L
## high 0.3333333 -0.5
## low 0.3333333 0.5
## mid -0.6666667 0.0
contrast.backness <- cbind(c(1/3,1/3,-2/3), c(-0.5,0.5,0)) #back,front,mid
colnames(contrast.backness) <- c("+BF-M","-B+F")
contrasts(yng$backness) <- contrast.backness
contrasts(yng$backness)
## +BF-M -B+F
## back 0.3333333 -0.5
## front 0.3333333 0.5
## mid -0.6666667 0.0
duration.mdl <- lmerTest::lmer(Duration ~ syllable * gender * register + (syllable * register|participant), data = yng)
## boundary (singular) fit: see help('isSingular')
summary(duration.mdl)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Duration ~ syllable * gender * register + (syllable * register |
## participant)
## Data: yng
##
## REML criterion at convergence: 12957.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9718 -0.5968 -0.0425 0.5419 4.6690
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 988.0 31.43
## syllable-closed+open 2525.9 50.26 0.52
## register-h+l 444.9 21.09 0.59 0.23
## syllable-closed+open:register-h+l 273.0 16.52 -0.16 -0.03
## Residual 1947.4 44.13
##
##
##
##
## 0.69
##
## Number of obs: 1234, groups: participant, 23
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 246.746 6.701 21.039
## syllable-closed+open 202.750 10.837 21.134
## gender+f-m 20.927 13.402 21.039
## register-h+l 20.969 5.178 21.443
## syllable-closed+open:gender+f-m 2.701 21.674 21.134
## syllable-closed+open:register-h+l -36.685 6.431 33.375
## gender+f-m:register-h+l -10.041 10.356 21.443
## syllable-closed+open:gender+f-m:register-h+l -7.675 12.863 33.375
## t value Pr(>|t|)
## (Intercept) 36.823 < 2e-16 ***
## syllable-closed+open 18.709 1.24e-14 ***
## gender+f-m 1.562 0.133315
## register-h+l 4.050 0.000558 ***
## syllable-closed+open:gender+f-m 0.125 0.901999
## syllable-closed+open:register-h+l -5.704 2.22e-06 ***
## gender+f-m:register-h+l -0.970 0.343081
## syllable-closed+open:gender+f-m:register-h+l -0.597 0.554751
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) syll-+ gndr+- rgst-+ sy-+:+- s-+:-+ g+-:-+
## syllbl-cls+ 0.476
## gender+f-m -0.042 -0.022
## registr-h+l 0.497 0.181 -0.016
## syllbl-+:+- -0.022 -0.041 0.476 -0.013
## syllbl-+:-+ -0.092 -0.002 -0.002 0.158 0.011
## gndr+f-m:-+ -0.016 -0.013 0.497 -0.031 0.181 -0.018
## syl-+:+-:-+ -0.002 0.011 -0.092 -0.018 -0.002 -0.015 0.158
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
duration.mdl <- lmerTest::lmer(Duration ~ syllable * gender * register + (syllable + register|participant), data = yng)
summary(duration.mdl)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Duration ~ syllable * gender * register + (syllable + register |
## participant)
## Data: yng
##
## REML criterion at convergence: 12963.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0185 -0.5747 -0.0411 0.5259 4.6285
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 983.2 31.36
## syllable-closed+open 2507.4 50.07 0.53
## register-h+l 546.2 23.37 0.52 0.19
## Residual 1962.1 44.30
## Number of obs: 1234, groups: participant, 23
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 246.788 6.686 21.044
## syllable-closed+open 202.642 10.803 21.151
## gender+f-m 21.055 13.373 21.044
## register-h+l 20.923 5.595 22.400
## syllable-closed+open:gender+f-m 2.450 21.606 21.151
## syllable-closed+open:register-h+l -36.810 5.441 1167.715
## gender+f-m:register-h+l -9.806 11.191 22.400
## syllable-closed+open:gender+f-m:register-h+l -7.836 10.883 1167.715
## t value Pr(>|t|)
## (Intercept) 36.909 < 2e-16 ***
## syllable-closed+open 18.758 1.16e-14 ***
## gender+f-m 1.574 0.13030
## register-h+l 3.739 0.00111 **
## syllable-closed+open:gender+f-m 0.113 0.91077
## syllable-closed+open:register-h+l -6.765 2.11e-11 ***
## gender+f-m:register-h+l -0.876 0.39019
## syllable-closed+open:gender+f-m:register-h+l -0.720 0.47166
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) syll-+ gndr+- rgst-+ sy-+:+- s-+:-+ g+-:-+
## syllbl-cls+ 0.483
## gender+f-m -0.042 -0.022
## registr-h+l 0.450 0.152 -0.014
## syllbl-+:+- -0.022 -0.041 0.483 -0.011
## syllbl-+:-+ -0.010 0.018 -0.007 -0.176 0.012
## gndr+f-m:-+ -0.014 -0.011 0.450 -0.032 0.152 -0.005
## syl-+:+-:-+ -0.007 0.012 -0.010 -0.005 0.018 -0.005 -0.176
library(emmeans)
summary(pairs(emmeans(duration.mdl, ~ syllable | gender | register), adjust = "bonferroni"))
## gender = female, register = high:
## contrast estimate SE df t.ratio p.value
## closed - open -224 15.3 23.3 -14.624 <.0001
##
## gender = male, register = high:
## contrast estimate SE df t.ratio p.value
## closed - open -218 16.0 23.4 -13.586 <.0001
##
## gender = female, register = low:
## contrast estimate SE df t.ratio p.value
## closed - open -184 15.6 24.7 -11.793 <.0001
##
## gender = male, register = low:
## contrast estimate SE df t.ratio p.value
## closed - open -185 16.1 23.7 -11.503 <.0001
##
## Degrees-of-freedom method: kenward-roger
summary(pairs(emmeans(duration.mdl, ~ register | syllable), adjust = "bonferroni"))
## NOTE: Results may be misleading due to involvement in interactions
## syllable = closed:
## contrast estimate SE df t.ratio p.value
## high - low -39.33 6.64 44.0 -5.921 <.0001
##
## syllable = open:
## contrast estimate SE df t.ratio p.value
## high - low -2.52 5.78 25.3 -0.436 0.6667
##
## Results are averaged over the levels of: gender
## Degrees-of-freedom method: kenward-roger
summary(pairs(emmeans(duration.mdl, ~ syllable | register), adjust = "bonferroni"))
## NOTE: Results may be misleading due to involvement in interactions
## register = high:
## contrast estimate SE df t.ratio p.value
## closed - open -221 11.1 23.4 -19.926 <.0001
##
## register = low:
## contrast estimate SE df t.ratio p.value
## closed - open -184 11.2 24.1 -16.467 <.0001
##
## Results are averaged over the levels of: gender
## Degrees-of-freedom method: kenward-roger
library(viridis)
## Loading required package: viridisLite
library(ggplot2)
library(hrbrthemes)
## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
## Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
## if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow
library(extrafont)
## Registering fonts with R
loadfonts(device = "win")
## Warning in loadfonts_win(quiet = quiet): OS is not Windows. No fonts registered
## with windowsFonts().
ggplot(yng, aes(x=factor(vowel, levels = c("i","I","y","Y","e","round_e", "E","schwa", "A", "u", "ou", "o","unround_o","low_o","a"), labels = c("i","ɪʔ","y","ʏʔ","e","ø", "ɛ","əʔ", "Aʔ", "u", "o", "oʔ","ɤ","ɔ","ɑ")), y=Duration, fill=vowel)) +
geom_violin() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
theme_ipsum() +
theme(
legend.position = "none",
plot.title = element_text(family="serif", face="bold", size=12),
axis.title.x = element_text(family = "serif"),
axis.title.y = element_text(family = "serif"),
axis.text.x = element_text(family = "serif"),
axis.text.y = element_text(family = "serif")
) +
ggtitle("Violin chart of the distribution of vowel duration") +
xlab("")
ggplot(yng, aes(x=factor(vowel, levels = c("i","I","y","Y","e","round_e", "E","schwa", "A", "u", "ou", "o","unround_o","low_o","a"), labels = c("i","ɪʔ","y","ʏʔ","e","ø", "ɛ","əʔ", "Aʔ", "u", "o", "oʔ","ɤ","ɔ","ɑ")), y=Duration, fill=register)) +
ylab("duration (ms)") +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.5, option="G") +
theme_ipsum() +
theme(
legend.position = "bottom",
#plot.title = element_text(family="Times", face="bold", size=12),
axis.title.x = element_text(family = "serif"),
axis.title.y = element_text(family = "serif", face="bold", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12),
legend.text = element_text(family = "serif", face="bold", size = 12)
) +
ggtitle("Boxplot of the distribution of vowel duration") +
xlab("")
ggplot(yng, aes(x=factor(vowel, levels = c("i","I","y","Y","e","round_e", "E","schwa", "A", "u", "ou", "o","unround_o","low_o","a"), labels = c("i","ɪʔ","y","ʏʔ","e","ø", "ɛ","əʔ", "Aʔ", "u", "o", "oʔ","ɤ","ɔ","ɑ")), y=Duration, fill=vowel)) +
xlab("") + ylab("duration (ms)") +
ggtitle("Boxplot of the distribution of vowel duration") +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.7, option="D") +
theme_ipsum() +
theme(
legend.position = "none",
#plot.title = element_text(family="Times", face="bold", size=12),
axis.title.x = element_text(family = "serif"),
axis.title.y = element_text(family = "serif", face="bold", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12)
)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
means <- yng %>%
group_by(vowel) %>%
summarise(mean_F1 = mean(F1_midpoint),
mean_F2 = mean(F2_midpoint))
ggplot(yng, aes(x = F2_midpoint, y = F1_midpoint
, color = vowel, label = factor(vowel, levels = c("i","I","y","Y","e","round_e", "E","schwa", "A", "u", "ou", "o","unround_o","low_o","a"), labels = c("i","ɪʔ","y","ʏʔ","e","ø", "ɛ","əʔ", "Aʔ", "u", "o", "oʔ","ɤ","ɔ","ɑ")))) +
geom_point(alpha = 0.1) +
stat_ellipse(level = 0.68) +
geom_text(data = means, aes(x = mean_F2, y = mean_F1, family="serif", fontface="bold"), size=12) +
scale_x_reverse(limits = c(3000, 500)) + scale_y_reverse(limits = c(1200, 200)) +
scale_color_discrete() +
guides(color = FALSE) +
theme_classic() +
theme(
axis.title.x = element_text(family = "serif", size = 12),
axis.title.y = element_text(family = "serif", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12))
## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 20 rows containing non-finite values (`stat_ellipse()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).
means75 <- yng %>%
group_by(vowel) %>%
summarise(mean_F175 = mean(F1_0.75point),
mean_F275 = mean(F2_0.75point))
ggplot(yng, aes(x = F2_0.75point, y = F1_0.75point
, color = vowel, label = factor(vowel, levels = c("i","I","y","Y","e","round_e", "E","schwa", "A", "u", "ou", "o","unround_o","low_o","a"), labels = c("i","ɪʔ","y","ʏʔ","e","ø", "ɛ","əʔ", "Aʔ", "u", "o", "oʔ","ɤ","ɔ","ɑ")))) +
geom_point(alpha = 0.1) +
stat_ellipse(level = 0.68) +
geom_text(data = means75, aes(x = mean_F275, y = mean_F175, family="serif", fontface="bold"), size=12) +
scale_x_reverse(limits = c(3000, 500)) + scale_y_reverse(limits = c(1200, 200)) +
scale_color_discrete() +
guides(color = FALSE) +
theme_classic() +
theme(
axis.title.x = element_text(family = "serif", size = 12),
axis.title.y = element_text(family = "serif", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12))
## Warning: Removed 23 rows containing non-finite values (`stat_ellipse()`).
## Warning: Removed 23 rows containing missing values (`geom_point()`).
library(dplyr)
library(ggpubr)
means50 <- yng %>%
group_by(vowel) %>%
summarise(mean_F150 = mean(F1_midpoint),
mean_F250 = mean(F2_midpoint))
means75 <- yng %>%
group_by(vowel) %>%
summarise(mean_F175 = mean(F1_0.75point),
mean_F275 = mean(F2_0.75point))
open_mid <- ggplot(yng[yng$syllable == "open",], aes(x = F2_midpoint, y = F1_midpoint
, color = vowel, label = factor(vowel, levels = c("i","y","e","round_e", "E", "u", "ou", "unround_o","low_o","a"), labels = c("i","y","e","ø", "ɛ", "u", "o", "ɤ","ɔ","ɑ")))) +
#xlab("F2 at the midpoint (Hz)") +
ylab("Open-ended vowels \n F1 (Hz)") +
ggtitle("Vowel space at the midpoint") +
geom_point(size = 0.95, alpha = 0.2) +
stat_ellipse(level = 0.68) +
geom_text(data = means50, aes(x = mean_F250, y = mean_F150, family="serif",
#fontface="bold"
), size = 10) +
scale_x_reverse(limits = c(3000, 500)) + scale_y_reverse(limits = c(1200, 200)) +
scale_color_discrete() +
guides(color = FALSE) +
theme_classic() +
theme(
plot.title = element_text(family = "serif", size = 14, hjust = 0.5),
axis.title.x = element_text(family = "serif", size = 12),
axis.title.y = element_text(family = "serif", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12))
open_upper <- ggplot(yng[yng$syllable == "open",], aes(x = F2_0.75point, y = F1_0.75point
, color = vowel, label = factor(vowel, levels = c("i","y","e","round_e", "E", "u", "ou", "unround_o","low_o","a"), labels = c("i","y","e","ø", "ɛ", "u", "o", "ɤ","ɔ","ɑ")))) +
#xlab("F2 at the upper quartile (Hz)") +
#ylab("Open-ended vowels \n F1 (Hz)") +
ggtitle("Vowel space at the upper quartile") +
geom_point(size = 0.95, alpha = 0.2) +
stat_ellipse(level = 0.68) +
geom_text(data = means75, aes(x = mean_F275, y = mean_F175, family="serif",
#fontface="bold"
), size = 10) +
scale_x_reverse(limits = c(3000, 500)) + scale_y_reverse(limits = c(1200, 200)) +
scale_color_discrete() +
guides(color = FALSE) +
theme_classic() +
theme(
plot.title = element_text(family = "serif", size = 14, hjust = 0.5),
axis.title.x = element_text(family = "serif", size = 12),
axis.title.y = element_text(family = "serif", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12))
closed_mid <- ggplot(yng[yng$syllable == "closed",], aes(x = F2_midpoint, y = F1_midpoint
, color = vowel, label = factor(vowel, levels = c("I","Y","schwa","A", "o"), labels = c("ɪ","ʏ","ə", "A", "o")))) +
xlab("F2 (Hz)") +
ylab("Glottal-ended vowels \n F1 (Hz)") +
#ggtitle("Vowel space at the midpoint of glottal-ended vowels") +
geom_point(size = 0.95, alpha = 0.2) +
stat_ellipse(level = 0.68) +
geom_text(data = means50, aes(x = mean_F250, y = mean_F150, family="serif",
#fontface="bold"
), size = 10) +
scale_x_reverse(limits = c(3000, 500)) + scale_y_reverse(limits = c(1200, 200)) +
scale_color_discrete() +
guides(color = FALSE) +
theme_classic() +
theme(
plot.title = element_text(family = "serif", size = 14, hjust = 0.5),
axis.title.x = element_text(family = "serif", size = 12),
axis.title.y = element_text(family = "serif", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12))
closed_upper <- ggplot(yng[yng$syllable == "closed",], aes(x = F2_0.75point, y = F1_0.75point
, color = vowel, label = factor(vowel, levels = c("I","Y","schwa","A", "o"), labels = c("ɪ","ʏ","ə", "A", "o")))) +
xlab("F2 (Hz)") +
#ylab("F1 at the upper quartile (Hz)") +
#ggtitle("Vowel space at the upper quartile of glottal-ended vowels") +
geom_point(size = 0.95, alpha = 0.2) +
stat_ellipse(level = 0.68) +
geom_text(data = means75, aes(x = mean_F275, y = mean_F175, family="serif",
#fontface="bold"
), size = 10) +
scale_x_reverse(limits = c(3000, 500)) + scale_y_reverse(limits = c(1200, 200)) +
scale_color_discrete() +
guides(color = FALSE) +
theme_classic() +
theme(
plot.title = element_text(family = "serif", size = 14, hjust = 0.5),
axis.title.x = element_text(family = "serif", size = 12),
axis.title.y = element_text(family = "serif", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12))
ggarrange(open_mid + rremove("x.title"), open_upper + rremove("x.title") +rremove("y.title") , closed_mid, closed_upper+rremove("y.title") ,
ncol = 2, nrow = 2)
## Warning: Removed 19 rows containing non-finite values (`stat_ellipse()`).
## Warning: Removed 19 rows containing missing values (`geom_point()`).
## Warning: Removed 5 rows containing missing values (`geom_text()`).
## Warning: Removed 18 rows containing non-finite values (`stat_ellipse()`).
## Warning: Removed 18 rows containing missing values (`geom_point()`).
## Warning: Removed 5 rows containing missing values (`geom_text()`).
## Warning: Removed 1 rows containing non-finite values (`stat_ellipse()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 10 rows containing missing values (`geom_text()`).
## Warning: Removed 5 rows containing non-finite values (`stat_ellipse()`).
## Warning: Removed 5 rows containing missing values (`geom_point()`).
## Warning: Removed 10 rows containing missing values (`geom_text()`).
ggplot(yng[yng$syllable == "open",], aes(x = log(F2_midpoint), y = log(F1_midpoint)
, color = vowel, label = factor(vowel, levels = c("i","y","e","round_e", "E", "u", "ou", "unround_o","low_o","a"), labels = c("i","y","e","ø", "ɛ", "u", "o", "ɤ","ɔ","ɑ")))) +
xlab("F2 at the midpoint (Hz)") +
ylab("F1 at the midpoint (Hz)") +
geom_point(alpha = 0.1) +
stat_ellipse(level = 0.68) +
geom_text(data = means, aes(x = log(mean_F2), y = log(mean_F1), family="serif", fontface="bold"), size=12) +
scale_x_reverse(limits = c(log(3000), log(500))) + scale_y_reverse(limits = c(log(1200), log(200))) +
scale_color_discrete() +
guides(color = FALSE) +
theme(
axis.title.x = element_text(family = "serif", size = 12),
axis.title.y = element_text(family = "serif", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12))+
theme_classic()
## Warning: Removed 19 rows containing non-finite values (`stat_ellipse()`).
## Warning: Removed 19 rows containing missing values (`geom_point()`).
## Warning: Removed 5 rows containing missing values (`geom_text()`).
F1mid.mdl <- lmerTest::lmer(F1_midpoint ~ syllable * gender * register * highness + (syllable + register|participant), data = yng)
summary(F1mid.mdl)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: F1_midpoint ~ syllable * gender * register * highness + (syllable +
## register | participant)
## Data: yng
##
## REML criterion at convergence: 14377.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6354 -0.6713 -0.0054 0.6203 6.0036
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 1695.2 41.17
## syllable-closed+open 2603.4 51.02 -0.58
## register-h+l 324.6 18.02 -0.16 -0.47
## Residual 7321.2 85.56
## Number of obs: 1234, groups: participant, 23
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 647.138 9.166
## syllable-closed+open -109.937 12.410
## gender+f-m 76.548 18.333
## register-h+l 12.583 7.399
## highness+HL-M 62.532 6.834
## highness-H+L 432.757 7.656
## syllable-closed+open:gender+f-m 11.187 24.821
## syllable-closed+open:register-h+l -4.638 12.721
## gender+f-m:register-h+l -13.918 14.797
## syllable-closed+open:highness+HL-M 250.745 13.668
## syllable-closed+open:highness-H+L 344.287 15.310
## gender+f-m:highness+HL-M 18.775 13.668
## gender+f-m:highness-H+L -9.170 15.312
## register-h+l:highness+HL-M -10.303 13.672
## register-h+l:highness-H+L 6.387 15.318
## syllable-closed+open:gender+f-m:register-h+l 22.197 25.441
## syllable-closed+open:gender+f-m:highness+HL-M 3.885 27.336
## syllable-closed+open:gender+f-m:highness-H+L 108.357 30.620
## syllable-closed+open:register-h+l:highness+HL-M 57.292 27.337
## syllable-closed+open:register-h+l:highness-H+L 95.393 30.637
## gender+f-m:register-h+l:highness+HL-M 16.988 27.345
## gender+f-m:register-h+l:highness-H+L -2.951 30.636
## syllable-closed+open:gender+f-m:register-h+l:highness+HL-M 4.933 54.673
## syllable-closed+open:gender+f-m:register-h+l:highness-H+L 91.579 61.274
## df t value
## (Intercept) 22.867 70.600
## syllable-closed+open 24.889 -8.858
## gender+f-m 22.867 4.176
## register-h+l 41.673 1.701
## highness+HL-M 1153.819 9.150
## highness-H+L 1155.245 56.526
## syllable-closed+open:gender+f-m 24.889 0.451
## syllable-closed+open:register-h+l 1155.221 -0.365
## gender+f-m:register-h+l 41.673 -0.941
## syllable-closed+open:highness+HL-M 1153.359 18.346
## syllable-closed+open:highness-H+L 1152.381 22.488
## gender+f-m:highness+HL-M 1153.819 1.374
## gender+f-m:highness-H+L 1155.245 -0.599
## register-h+l:highness+HL-M 1154.495 -0.754
## register-h+l:highness-H+L 1152.438 0.417
## syllable-closed+open:gender+f-m:register-h+l 1155.221 0.872
## syllable-closed+open:gender+f-m:highness+HL-M 1153.359 0.142
## syllable-closed+open:gender+f-m:highness-H+L 1152.381 3.539
## syllable-closed+open:register-h+l:highness+HL-M 1152.297 2.096
## syllable-closed+open:register-h+l:highness-H+L 1154.494 3.114
## gender+f-m:register-h+l:highness+HL-M 1154.495 0.621
## gender+f-m:register-h+l:highness-H+L 1152.438 -0.096
## syllable-closed+open:gender+f-m:register-h+l:highness+HL-M 1152.297 0.090
## syllable-closed+open:gender+f-m:register-h+l:highness-H+L 1154.494 1.495
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## syllable-closed+open 3.63e-09 ***
## gender+f-m 0.000367 ***
## register-h+l 0.096458 .
## highness+HL-M < 2e-16 ***
## highness-H+L < 2e-16 ***
## syllable-closed+open:gender+f-m 0.656102
## syllable-closed+open:register-h+l 0.715498
## gender+f-m:register-h+l 0.352345
## syllable-closed+open:highness+HL-M < 2e-16 ***
## syllable-closed+open:highness-H+L < 2e-16 ***
## gender+f-m:highness+HL-M 0.169811
## gender+f-m:highness-H+L 0.549385
## register-h+l:highness+HL-M 0.451269
## register-h+l:highness-H+L 0.676805
## syllable-closed+open:gender+f-m:register-h+l 0.383131
## syllable-closed+open:gender+f-m:highness+HL-M 0.887004
## syllable-closed+open:gender+f-m:highness-H+L 0.000418 ***
## syllable-closed+open:register-h+l:highness+HL-M 0.036317 *
## syllable-closed+open:register-h+l:highness-H+L 0.001893 **
## gender+f-m:register-h+l:highness+HL-M 0.534553
## gender+f-m:register-h+l:highness-H+L 0.923274
## syllable-closed+open:gender+f-m:register-h+l:highness+HL-M 0.928126
## syllable-closed+open:gender+f-m:register-h+l:highness-H+L 0.135293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 24 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
summary(pairs(emmeans(F1mid.mdl, ~ register | highness | syllable), adjust = "bonferroni"))
## NOTE: Results may be misleading due to involvement in interactions
## highness = high, syllable = closed:
## contrast estimate SE df t.ratio p.value
## high - low -22.57 11.55 226.5 -1.955 0.0518
##
## highness = low, syllable = closed:
## contrast estimate SE df t.ratio p.value
## high - low 18.74 20.04 850.6 0.935 0.3501
##
## highness = mid, syllable = closed:
## contrast estimate SE df t.ratio p.value
## high - low -40.87 21.44 935.1 -1.906 0.0569
##
## highness = high, syllable = open:
## contrast estimate SE df t.ratio p.value
## high - low 10.66 10.35 148.9 1.031 0.3044
##
## highness = low, syllable = open:
## contrast estimate SE df t.ratio p.value
## high - low -43.42 18.89 800.3 -2.299 0.0218
##
## highness = mid, syllable = open:
## contrast estimate SE df t.ratio p.value
## high - low 1.96 9.08 94.7 0.216 0.8292
##
## Results are averaged over the levels of: gender
## Degrees-of-freedom method: kenward-roger
summary(pairs(emmeans(F1mid.mdl, ~ highness | gender | syllable), adjust = "bonferroni"))
## NOTE: Results may be misleading due to involvement in interactions
## gender = female, syllable = closed:
## contrast estimate SE df t.ratio p.value
## high - low -228.9 15.94 1155 -14.366 <.0001
## high - mid -168.9 17.09 1154 -9.883 <.0001
## low - mid 60.0 20.75 1156 2.894 0.0116
##
## gender = male, syllable = closed:
## contrast estimate SE df t.ratio p.value
## high - low -292.3 15.81 1153 -18.487 <.0001
## high - mid -217.4 16.45 1152 -13.217 <.0001
## low - mid 74.9 20.01 1151 3.742 0.0006
##
## gender = female, syllable = open:
## contrast estimate SE df t.ratio p.value
## high - low -627.4 14.47 1153 -43.346 <.0001
## high - mid -115.4 8.93 1160 -12.922 <.0001
## low - mid 512.0 14.00 1151 36.579 <.0001
##
## gender = male, syllable = open:
## contrast estimate SE df t.ratio p.value
## high - low -582.4 15.00 1152 -38.814 <.0001
## high - mid -113.7 9.03 1151 -12.579 <.0001
## low - mid 468.7 14.64 1150 32.017 <.0001
##
## Results are averaged over the levels of: register
## Degrees-of-freedom method: kenward-roger
## P value adjustment: bonferroni method for 3 tests
lowplot <- ggplot(yng[yng$highness == "low", ], aes(x=gender, labels = gender, y=F1_midpoint, fill=syllable)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.5, option="C") +
theme_classic() + scale_y_reverse(limits = c(1200, 200)) +
theme(
legend.position = "bottom",
axis.title.x = element_text(family = "serif", face="bold", size = 12),
axis.title.y = element_text(family = "serif", face="bold", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12),
legend.text = element_text(family = "serif", face="bold", size = 12)
) +
xlab("low vowels") +
ylab("F1 at the midpoint (Hz)")
midplot <- ggplot(yng[yng$highness == "mid", ], aes(x=gender, labels = gender, y=F1_midpoint, fill=syllable)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.5, option="C") +
theme_classic() + scale_y_reverse(limits = c(1200, 200)) +
theme(
legend.position = "bottom",
#plot.title = element_text(family="Times", face="bold", size=12),
axis.title.x = element_text(family = "serif", face="bold", size = 12),
axis.title.y = element_text(family = "serif", face="bold", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12),
legend.text = element_text(family = "serif", face="bold", size = 12)
) +
xlab("mid vowels")
highplot <- ggplot(yng[yng$highness == "high", ], aes(x=gender, labels = gender, y=F1_midpoint, fill=syllable)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.5, option="C") +
theme_classic() + scale_y_reverse(limits = c(1200, 200)) +
theme(
legend.position = "bottom",
axis.title.x = element_text(family = "serif", face="bold", size = 12),
axis.title.y = element_text(family = "serif", face="bold", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12),
legend.text = element_text(family = "serif", face="bold", size = 12)
) +
xlab("high vowels")
ggarrange(lowplot, midplot + rremove("y.title") , highplot + rremove("y.title") ,
ncol = 3, nrow = 1)
## Warning: Removed 4 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 3 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 5 rows containing non-finite values (`stat_boxplot()`).
F1upper.mdl <- lmerTest::lmer(F1_0.75point ~ syllable * gender * register * highness + (syllable |participant), data = yng)
summary(F1upper.mdl)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: F1_0.75point ~ syllable * gender * register * highness + (syllable |
## participant)
## Data: yng
##
## REML criterion at convergence: 14741.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.8622 -0.6070 -0.0513 0.5999 5.5176
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 2714 52.10
## syllable-closed+open 3677 60.64 -0.71
## Residual 9950 99.75
## Number of obs: 1234, groups: participant, 23
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 648.656 11.491
## syllable-closed+open -143.881 14.674
## gender+f-m 80.116 22.982
## register-h+l 17.454 7.415
## highness+HL-M 66.305 7.963
## highness-H+L 382.935 8.920
## syllable-closed+open:gender+f-m 13.123 29.347
## syllable-closed+open:register-h+l 9.492 14.825
## gender+f-m:register-h+l -38.515 14.830
## syllable-closed+open:highness+HL-M 210.611 15.924
## syllable-closed+open:highness-H+L 331.691 17.842
## gender+f-m:highness+HL-M 29.942 15.927
## gender+f-m:highness-H+L -13.548 17.840
## register-h+l:highness+HL-M -6.272 15.928
## register-h+l:highness-H+L -5.048 17.853
## syllable-closed+open:gender+f-m:register-h+l 11.576 29.650
## syllable-closed+open:gender+f-m:highness+HL-M -30.619 31.848
## syllable-closed+open:gender+f-m:highness-H+L 56.113 35.685
## syllable-closed+open:register-h+l:highness+HL-M 66.013 31.857
## syllable-closed+open:register-h+l:highness-H+L 142.090 35.699
## gender+f-m:register-h+l:highness+HL-M -18.809 31.856
## gender+f-m:register-h+l:highness-H+L -95.874 35.706
## syllable-closed+open:gender+f-m:register-h+l:highness+HL-M -41.678 63.713
## syllable-closed+open:gender+f-m:register-h+l:highness-H+L 25.743 71.399
## df t value
## (Intercept) 22.624 56.450
## syllable-closed+open 25.093 -9.805
## gender+f-m 22.624 3.486
## register-h+l 1171.665 2.354
## highness+HL-M 1170.488 8.326
## highness-H+L 1170.571 42.930
## syllable-closed+open:gender+f-m 25.093 0.447
## syllable-closed+open:register-h+l 1172.848 0.640
## gender+f-m:register-h+l 1171.665 -2.597
## syllable-closed+open:highness+HL-M 1171.086 13.226
## syllable-closed+open:highness-H+L 1170.027 18.590
## gender+f-m:highness+HL-M 1170.488 1.880
## gender+f-m:highness-H+L 1170.571 -0.759
## register-h+l:highness+HL-M 1170.588 -0.394
## register-h+l:highness-H+L 1170.893 -0.283
## syllable-closed+open:gender+f-m:register-h+l 1172.848 0.390
## syllable-closed+open:gender+f-m:highness+HL-M 1171.086 -0.961
## syllable-closed+open:gender+f-m:highness-H+L 1170.027 1.572
## syllable-closed+open:register-h+l:highness+HL-M 1170.591 2.072
## syllable-closed+open:register-h+l:highness-H+L 1171.574 3.980
## gender+f-m:register-h+l:highness+HL-M 1170.588 -0.590
## gender+f-m:register-h+l:highness-H+L 1170.893 -2.685
## syllable-closed+open:gender+f-m:register-h+l:highness+HL-M 1170.591 -0.654
## syllable-closed+open:gender+f-m:register-h+l:highness-H+L 1171.574 0.361
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## syllable-closed+open 4.58e-10 ***
## gender+f-m 0.00203 **
## register-h+l 0.01874 *
## highness+HL-M 2.31e-16 ***
## highness-H+L < 2e-16 ***
## syllable-closed+open:gender+f-m 0.65859
## syllable-closed+open:register-h+l 0.52214
## gender+f-m:register-h+l 0.00952 **
## syllable-closed+open:highness+HL-M < 2e-16 ***
## syllable-closed+open:highness-H+L < 2e-16 ***
## gender+f-m:highness+HL-M 0.06036 .
## gender+f-m:highness-H+L 0.44776
## register-h+l:highness+HL-M 0.69380
## register-h+l:highness-H+L 0.77741
## syllable-closed+open:gender+f-m:register-h+l 0.69629
## syllable-closed+open:gender+f-m:highness+HL-M 0.33655
## syllable-closed+open:gender+f-m:highness-H+L 0.11611
## syllable-closed+open:register-h+l:highness+HL-M 0.03847 *
## syllable-closed+open:register-h+l:highness-H+L 7.31e-05 ***
## gender+f-m:register-h+l:highness+HL-M 0.55503
## gender+f-m:register-h+l:highness-H+L 0.00735 **
## syllable-closed+open:gender+f-m:register-h+l:highness+HL-M 0.51314
## syllable-closed+open:gender+f-m:register-h+l:highness-H+L 0.71850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 24 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
summary(pairs(emmeans(F1upper.mdl, ~ syllable | register | highness), adjust = "bonferroni"))
## NOTE: Results may be misleading due to involvement in interactions
## register = high, highness = high:
## contrast estimate SE df t.ratio p.value
## closed - open 219.7 17.3 47.7 12.707 <.0001
##
## register = low, highness = high:
## contrast estimate SE df t.ratio p.value
## closed - open 259.3 17.6 50.9 14.747 <.0001
##
## register = high, highness = low:
## contrast estimate SE df t.ratio p.value
## closed - open -40.9 25.0 194.0 -1.633 0.1041
##
## register = low, highness = low:
## contrast estimate SE df t.ratio p.value
## closed - open -143.4 26.1 224.0 -5.487 <.0001
##
## register = high, highness = mid:
## contrast estimate SE df t.ratio p.value
## closed - open 267.0 20.8 97.9 12.839 <.0001
##
## register = low, highness = mid:
## contrast estimate SE df t.ratio p.value
## closed - open 301.5 24.2 170.8 12.474 <.0001
##
## Results are averaged over the levels of: gender
## Degrees-of-freedom method: kenward-roger
summary(pairs(emmeans(F1upper.mdl, ~ register | highness | syllable), adjust = "bonferroni"))
## NOTE: Results may be misleading due to involvement in interactions
## highness = high, syllable = closed:
## contrast estimate SE df t.ratio p.value
## high - low -37.66 12.71 1171 -2.962 0.0031
##
## highness = low, syllable = closed:
## contrast estimate SE df t.ratio p.value
## high - low 38.43 22.90 1172 1.678 0.0936
##
## highness = mid, syllable = closed:
## contrast estimate SE df t.ratio p.value
## high - low -38.89 24.58 1171 -1.582 0.1139
##
## highness = high, syllable = open:
## contrast estimate SE df t.ratio p.value
## high - low 1.89 11.20 1170 0.169 0.8662
##
## highness = low, syllable = open:
## contrast estimate SE df t.ratio p.value
## high - low -64.11 21.57 1170 -2.972 0.0030
##
## highness = mid, syllable = open:
## contrast estimate SE df t.ratio p.value
## high - low -4.38 9.63 1170 -0.454 0.6496
##
## Results are averaged over the levels of: gender
## Degrees-of-freedom method: kenward-roger
summary(pairs(emmeans(F1upper.mdl, ~ gender | register| highness ), adjust = "bonferroni"))
## NOTE: Results may be misleading due to involvement in interactions
## register = high, highness = high:
## contrast estimate SE df t.ratio p.value
## female - male 95.3 24.7 30.1 3.853 0.0006
##
## register = low, highness = high:
## contrast estimate SE df t.ratio p.value
## female - male 98.4 24.9 31.1 3.947 0.0004
##
## register = high, highness = low:
## contrast estimate SE df t.ratio p.value
## female - male 129.7 30.7 70.2 4.229 0.0001
##
## register = low, highness = low:
## contrast estimate SE df t.ratio p.value
## female - male 37.0 31.6 78.5 1.171 0.2452
##
## register = high, highness = mid:
## contrast estimate SE df t.ratio p.value
## female - male 73.1 27.3 44.6 2.679 0.0103
##
## register = low, highness = mid:
## contrast estimate SE df t.ratio p.value
## female - male 47.2 30.0 64.1 1.575 0.1202
##
## Results are averaged over the levels of: syllable
## Degrees-of-freedom method: kenward-roger
F2mid.mdl <- lmerTest::lmer(F2_midpoint ~ gender * syllable * register * backness + (syllable|participant), data = yng)
summary(F2mid.mdl)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: F2_midpoint ~ gender * syllable * register * backness + (syllable |
## participant)
## Data: yng
##
## REML criterion at convergence: 16920.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2307 -0.4939 0.0353 0.4931 4.0993
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 6492 80.57
## syllable-closed+open 1247 35.31 -0.32
## Residual 62002 249.00
## Number of obs: 1234, groups: participant, 23
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1429.622 18.657
## gender+f-m 153.397 37.315
## syllable-closed+open -18.746 17.694
## register-h+l 9.712 16.102
## backness+BF-M 18.181 17.106
## backness-B+F 1153.138 19.623
## gender+f-m:syllable-closed+open -38.223 35.388
## gender+f-m:register-h+l -7.353 32.203
## syllable-closed+open:register-h+l 2.970 32.168
## gender+f-m:backness+BF-M 48.357 34.211
## gender+f-m:backness-B+F 130.695 39.246
## syllable-closed+open:backness+BF-M 136.029 34.208
## syllable-closed+open:backness-B+F 369.004 39.246
## register-h+l:backness+BF-M 10.289 34.226
## register-h+l:backness-B+F -41.752 39.220
## gender+f-m:syllable-closed+open:register-h+l -29.176 64.336
## gender+f-m:syllable-closed+open:backness+BF-M 43.302 68.415
## gender+f-m:syllable-closed+open:backness-B+F 121.448 78.493
## gender+f-m:register-h+l:backness+BF-M -66.766 68.453
## gender+f-m:register-h+l:backness-B+F -25.497 78.441
## syllable-closed+open:register-h+l:backness+BF-M -40.633 68.440
## syllable-closed+open:register-h+l:backness-B+F -16.992 78.474
## gender+f-m:syllable-closed+open:register-h+l:backness+BF-M 49.203 136.881
## gender+f-m:syllable-closed+open:register-h+l:backness-B+F 12.552 156.949
## df t value
## (Intercept) 21.806 76.625
## gender+f-m 21.806 4.111
## syllable-closed+open 24.513 -1.059
## register-h+l 1178.246 0.603
## backness+BF-M 1172.278 1.063
## backness-B+F 1177.375 58.764
## gender+f-m:syllable-closed+open 24.513 -1.080
## gender+f-m:register-h+l 1178.246 -0.228
## syllable-closed+open:register-h+l 1180.429 0.092
## gender+f-m:backness+BF-M 1172.278 1.413
## gender+f-m:backness-B+F 1177.375 3.330
## syllable-closed+open:backness+BF-M 1172.471 3.977
## syllable-closed+open:backness-B+F 1176.773 9.402
## register-h+l:backness+BF-M 1174.575 0.301
## register-h+l:backness-B+F 1175.631 -1.065
## gender+f-m:syllable-closed+open:register-h+l 1180.429 -0.453
## gender+f-m:syllable-closed+open:backness+BF-M 1172.471 0.633
## gender+f-m:syllable-closed+open:backness-B+F 1176.773 1.547
## gender+f-m:register-h+l:backness+BF-M 1174.575 -0.975
## gender+f-m:register-h+l:backness-B+F 1175.631 -0.325
## syllable-closed+open:register-h+l:backness+BF-M 1175.137 -0.594
## syllable-closed+open:register-h+l:backness-B+F 1173.959 -0.217
## gender+f-m:syllable-closed+open:register-h+l:backness+BF-M 1175.137 0.359
## gender+f-m:syllable-closed+open:register-h+l:backness-B+F 1173.959 0.080
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender+f-m 0.000467 ***
## syllable-closed+open 0.299721
## register-h+l 0.546495
## backness+BF-M 0.288053
## backness-B+F < 2e-16 ***
## gender+f-m:syllable-closed+open 0.290605
## gender+f-m:register-h+l 0.819436
## syllable-closed+open:register-h+l 0.926460
## gender+f-m:backness+BF-M 0.157782
## gender+f-m:backness-B+F 0.000895 ***
## syllable-closed+open:backness+BF-M 7.42e-05 ***
## syllable-closed+open:backness-B+F < 2e-16 ***
## register-h+l:backness+BF-M 0.763771
## register-h+l:backness-B+F 0.287301
## gender+f-m:syllable-closed+open:register-h+l 0.650283
## gender+f-m:syllable-closed+open:backness+BF-M 0.526900
## gender+f-m:syllable-closed+open:backness-B+F 0.122072
## gender+f-m:register-h+l:backness+BF-M 0.329585
## gender+f-m:register-h+l:backness-B+F 0.745203
## syllable-closed+open:register-h+l:backness+BF-M 0.552828
## syllable-closed+open:register-h+l:backness-B+F 0.828616
## gender+f-m:syllable-closed+open:register-h+l:backness+BF-M 0.719317
## gender+f-m:syllable-closed+open:register-h+l:backness-B+F 0.936271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 24 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
summary(pairs(emmeans(F2mid.mdl, ~ backness | syllable ), adjust = "bonferroni"))
## NOTE: Results may be misleading due to involvement in interactions
## syllable = closed:
## contrast estimate SE df t.ratio p.value
## back - front -969 33.7 1179 -28.705 <.0001
## back - mid -534 34.6 1176 -15.458 <.0001
## front - mid 434 28.4 1175 15.288 <.0001
##
## syllable = open:
## contrast estimate SE df t.ratio p.value
## back - front -1338 20.1 1171 -66.605 <.0001
## back - mid -583 24.6 1172 -23.644 <.0001
## front - mid 755 22.4 1169 33.634 <.0001
##
## Results are averaged over the levels of: gender, register
## Degrees-of-freedom method: kenward-roger
## P value adjustment: bonferroni method for 3 tests
summary(pairs(emmeans(F2mid.mdl, ~ syllable | backness ), adjust = "bonferroni"))
## NOTE: Results may be misleading due to involvement in interactions
## backness = back:
## contrast estimate SE df t.ratio p.value
## closed - open 158 32.7 250.2 4.826 <.0001
##
## backness = front:
## contrast estimate SE df t.ratio p.value
## closed - open -211 24.1 83.5 -8.760 <.0001
##
## backness = mid:
## contrast estimate SE df t.ratio p.value
## closed - open 109 29.0 166.3 3.771 0.0002
##
## Results are averaged over the levels of: gender, register
## Degrees-of-freedom method: kenward-roger
F2upper.mdl <- lmerTest::lmer(F2_0.75point ~ gender * syllable * register * backness + (syllable|participant), data = yng)
summary(F2upper.mdl)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: F2_0.75point ~ gender * syllable * register * backness + (syllable |
## participant)
## Data: yng
##
## REML criterion at convergence: 16946.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4797 -0.5358 0.0095 0.5306 4.9307
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 8049 89.72
## syllable-closed+open 2083 45.64 -0.40
## Residual 63075 251.15
## Number of obs: 1234, groups: participant, 23
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1419.672 20.423
## gender+f-m 146.901 40.846
## syllable-closed+open -8.997 18.823
## register-h+l 6.697 16.246
## backness+BF-M 43.273 17.255
## backness-B+F 1095.156 19.798
## gender+f-m:syllable-closed+open -10.589 37.645
## gender+f-m:register-h+l -8.668 32.492
## syllable-closed+open:register-h+l 28.830 32.458
## gender+f-m:backness+BF-M 40.913 34.510
## gender+f-m:backness-B+F 107.697 39.595
## syllable-closed+open:backness+BF-M 196.118 34.506
## syllable-closed+open:backness-B+F 485.237 39.595
## register-h+l:backness+BF-M 4.102 34.528
## register-h+l:backness-B+F -55.939 39.566
## gender+f-m:syllable-closed+open:register-h+l 13.443 64.916
## gender+f-m:syllable-closed+open:backness+BF-M 48.408 69.013
## gender+f-m:syllable-closed+open:backness-B+F 200.506 79.190
## gender+f-m:register-h+l:backness+BF-M 6.755 69.056
## gender+f-m:register-h+l:backness-B+F -42.543 79.133
## syllable-closed+open:register-h+l:backness+BF-M -55.854 69.044
## syllable-closed+open:register-h+l:backness-B+F 63.425 79.165
## gender+f-m:syllable-closed+open:register-h+l:backness+BF-M 62.572 138.088
## gender+f-m:syllable-closed+open:register-h+l:backness-B+F 20.815 158.330
## df t value
## (Intercept) 21.625 69.513
## gender+f-m 21.625 3.596
## syllable-closed+open 24.172 -0.478
## register-h+l 1177.166 0.412
## backness+BF-M 1171.871 2.508
## backness-B+F 1176.516 55.317
## gender+f-m:syllable-closed+open 24.172 -0.281
## gender+f-m:register-h+l 1177.166 -0.267
## syllable-closed+open:register-h+l 1179.485 0.888
## gender+f-m:backness+BF-M 1171.871 1.186
## gender+f-m:backness-B+F 1176.516 2.720
## syllable-closed+open:backness+BF-M 1172.085 5.684
## syllable-closed+open:backness-B+F 1176.002 12.255
## register-h+l:backness+BF-M 1173.905 0.119
## register-h+l:backness-B+F 1174.934 -1.414
## gender+f-m:syllable-closed+open:register-h+l 1179.485 0.207
## gender+f-m:syllable-closed+open:backness+BF-M 1172.085 0.701
## gender+f-m:syllable-closed+open:backness-B+F 1176.002 2.532
## gender+f-m:register-h+l:backness+BF-M 1173.905 0.098
## gender+f-m:register-h+l:backness-B+F 1174.934 -0.538
## syllable-closed+open:register-h+l:backness+BF-M 1174.464 -0.809
## syllable-closed+open:register-h+l:backness-B+F 1173.327 0.801
## gender+f-m:syllable-closed+open:register-h+l:backness+BF-M 1174.464 0.453
## gender+f-m:syllable-closed+open:register-h+l:backness-B+F 1173.327 0.131
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## gender+f-m 0.00164 **
## syllable-closed+open 0.63696
## register-h+l 0.68025
## backness+BF-M 0.01228 *
## backness-B+F < 2e-16 ***
## gender+f-m:syllable-closed+open 0.78088
## gender+f-m:register-h+l 0.78969
## syllable-closed+open:register-h+l 0.37460
## gender+f-m:backness+BF-M 0.23604
## gender+f-m:backness-B+F 0.00663 **
## syllable-closed+open:backness+BF-M 1.66e-08 ***
## syllable-closed+open:backness-B+F < 2e-16 ***
## register-h+l:backness+BF-M 0.90544
## register-h+l:backness-B+F 0.15769
## gender+f-m:syllable-closed+open:register-h+l 0.83598
## gender+f-m:syllable-closed+open:backness+BF-M 0.48318
## gender+f-m:syllable-closed+open:backness-B+F 0.01147 *
## gender+f-m:register-h+l:backness+BF-M 0.92209
## gender+f-m:register-h+l:backness-B+F 0.59095
## syllable-closed+open:register-h+l:backness+BF-M 0.41870
## syllable-closed+open:register-h+l:backness-B+F 0.42319
## gender+f-m:syllable-closed+open:register-h+l:backness+BF-M 0.65054
## gender+f-m:syllable-closed+open:register-h+l:backness-B+F 0.89543
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 24 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
summary(pairs(emmeans(F2upper.mdl, ~ gender | backness | syllable ), adjust = "bonferroni"))
## NOTE: Results may be misleading due to involvement in interactions
## backness = back, syllable = closed:
## contrast estimate SE df t.ratio p.value
## female - male 154.0 69.9 83.2 2.204 0.0303
##
## backness = front, syllable = closed:
## contrast estimate SE df t.ratio p.value
## female - male 161.5 57.6 39.0 2.806 0.0078
##
## backness = mid, syllable = closed:
## contrast estimate SE df t.ratio p.value
## female - male 141.1 59.6 44.6 2.367 0.0223
##
## backness = back, syllable = open:
## contrast estimate SE df t.ratio p.value
## female - male 59.3 47.4 45.8 1.253 0.2167
##
## backness = front, syllable = open:
## contrast estimate SE df t.ratio p.value
## female - male 267.3 42.7 30.4 6.266 <.0001
##
## backness = mid, syllable = open:
## contrast estimate SE df t.ratio p.value
## female - male 98.2 51.4 63.6 1.909 0.0608
##
## Results are averaged over the levels of: register
## Degrees-of-freedom method: kenward-roger
# t-test for F1
for (vowel in levels(yng$vowel)){
t_test <- t.test(yng[yng$vowel == vowel,"F1_midpoint"], yng[yng$vowel == vowel,"F1_0.75point"],paired = TRUE)
print(vowel)
print(t_test)
}
## [1] "a"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = 2.8846, df = 85, p-value = 0.004965
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 14.92226 81.12425
## sample estimates:
## mean difference
## 48.02326
##
## [1] "A"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = -0.44354, df = 77, p-value = 0.6586
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -32.93645 20.93645
## sample estimates:
## mean difference
## -6
##
## [1] "e"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = 5.7602, df = 81, p-value = 1.459e-07
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 24.97783 51.33925
## sample estimates:
## mean difference
## 38.15854
##
## [1] "E"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = -2.6713, df = 86, p-value = 0.009037
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -32.037005 -4.698627
## sample estimates:
## mean difference
## -18.36782
##
## [1] "i"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = -1.7087, df = 81, p-value = 0.09133
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -24.072512 1.828609
## sample estimates:
## mean difference
## -11.12195
##
## [1] "I"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = -6.812, df = 80, p-value = 1.627e-09
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -74.21047 -40.65373
## sample estimates:
## mean difference
## -57.4321
##
## [1] "low_o"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = -2.6576, df = 84, p-value = 0.009418
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -33.114088 -4.768265
## sample estimates:
## mean difference
## -18.94118
##
## [1] "o"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = -3.8145, df = 82, p-value = 0.0002635
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -49.78858 -15.65721
## sample estimates:
## mean difference
## -32.72289
##
## [1] "ou"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = -1.4064, df = 81, p-value = 0.1634
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -23.76454 4.08161
## sample estimates:
## mean difference
## -9.841463
##
## [1] "round_e"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = -3.2401, df = 86, p-value = 0.0017
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -22.571119 -5.405893
## sample estimates:
## mean difference
## -13.98851
##
## [1] "schwa"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = -0.31601, df = 70, p-value = 0.7529
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -22.96338 16.68169
## sample estimates:
## mean difference
## -3.140845
##
## [1] "u"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = 0.21302, df = 78, p-value = 0.8319
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -12.78287 15.84616
## sample estimates:
## mean difference
## 1.531646
##
## [1] "unround_o"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = 4.7554, df = 89, p-value = 7.586e-06
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 21.53997 52.46003
## sample estimates:
## mean difference
## 37
##
## [1] "y"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = -2.1631, df = 75, p-value = 0.03372
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -22.3184045 -0.9184376
## sample estimates:
## mean difference
## -11.61842
##
## [1] "Y"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F1_midpoint"] and yng[yng$vowel == vowel, "F1_0.75point"]
## t = -7.1445, df = 84, p-value = 3.031e-10
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -66.74451 -37.67902
## sample estimates:
## mean difference
## -52.21176
# t-test for F2
for (vowel in levels(yng$vowel)){
t_test <- t.test(yng[yng$vowel == vowel,"F2_midpoint"], yng[yng$vowel == vowel,"F2_0.75point"],paired = TRUE)
print(vowel)
print(t_test)
}
## [1] "a"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = 1.7296, df = 85, p-value = 0.08734
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -3.930798 56.488938
## sample estimates:
## mean difference
## 26.27907
##
## [1] "A"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = 0.31822, df = 77, p-value = 0.7512
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -20.15359 27.82025
## sample estimates:
## mean difference
## 3.833333
##
## [1] "e"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = -2.8162, df = 81, p-value = 0.006101
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -182.9500 -31.4646
## sample estimates:
## mean difference
## -107.2073
##
## [1] "E"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = 0.87961, df = 86, p-value = 0.3815
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -23.23057 60.10413
## sample estimates:
## mean difference
## 18.43678
##
## [1] "i"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = -0.27496, df = 81, p-value = 0.784
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -57.95505 43.88187
## sample estimates:
## mean difference
## -7.036585
##
## [1] "I"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = 6.3765, df = 80, p-value = 1.088e-08
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 95.76319 182.65657
## sample estimates:
## mean difference
## 139.2099
##
## [1] "low_o"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = -3.5368, df = 84, p-value = 0.0006622
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -50.32312 -14.10041
## sample estimates:
## mean difference
## -32.21176
##
## [1] "o"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = -4.935, df = 82, p-value = 4.143e-06
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -58.40637 -24.84664
## sample estimates:
## mean difference
## -41.62651
##
## [1] "ou"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = -1.5019, df = 81, p-value = 0.137
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -45.474257 6.352305
## sample estimates:
## mean difference
## -19.56098
##
## [1] "round_e"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = -1.3057, df = 86, p-value = 0.1951
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -54.71306 11.33375
## sample estimates:
## mean difference
## -21.68966
##
## [1] "schwa"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = 0.92456, df = 70, p-value = 0.3584
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -16.90132 46.11259
## sample estimates:
## mean difference
## 14.60563
##
## [1] "u"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = 0.83876, df = 78, p-value = 0.4042
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -21.99435 54.01966
## sample estimates:
## mean difference
## 16.01266
##
## [1] "unround_o"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = 4.1772, df = 89, p-value = 6.872e-05
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 29.72951 83.67049
## sample estimates:
## mean difference
## 56.7
##
## [1] "y"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = 3.9976, df = 75, p-value = 0.0001484
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 28.37774 84.75384
## sample estimates:
## mean difference
## 56.56579
##
## [1] "Y"
##
## Paired t-test
##
## data: yng[yng$vowel == vowel, "F2_midpoint"] and yng[yng$vowel == vowel, "F2_0.75point"]
## t = 1.2471, df = 84, p-value = 0.2158
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -8.463531 36.934119
## sample estimates:
## mean difference
## 14.23529
breathy <- lmerTest::lmer(H1.H2 ~ gender * syllable * register + (syllable + register |participant), data = yng)
summary(breathy)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: H1.H2 ~ gender * syllable * register + (syllable + register |
## participant)
## Data: yng
##
## REML criterion at convergence: 8364.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4610 -0.5101 -0.0552 0.4274 5.7931
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 20.087 4.482
## syllable-closed+open 2.018 1.421 0.83
## register-h+l 12.218 3.495 0.09 -0.23
## Residual 51.655 7.187
## Number of obs: 1221, groups: participant, 23
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -1.1321 0.9619 20.9291
## gender+f-m -2.0778 1.9238 20.9291
## syllable-closed+open 1.5509 0.5345 20.1932
## register-h+l -0.1604 0.8560 23.2329
## gender+f-m:syllable-closed+open 3.3615 1.0690 20.1932
## gender+f-m:register-h+l 0.4075 1.7120 23.2329
## syllable-closed+open:register-h+l 0.6622 0.8886 1165.0705
## gender+f-m:syllable-closed+open:register-h+l 1.9660 1.7772 1165.0705
## t value Pr(>|t|)
## (Intercept) -1.177 0.25242
## gender+f-m -1.080 0.29241
## syllable-closed+open 2.902 0.00876 **
## register-h+l -0.187 0.85298
## gender+f-m:syllable-closed+open 3.145 0.00506 **
## gender+f-m:register-h+l 0.238 0.81397
## syllable-closed+open:register-h+l 0.745 0.45628
## gender+f-m:syllable-closed+open:register-h+l 1.106 0.26886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr+- syll-+ rgst-+ gndr+f-m:s-+ gndr+f-m:r-+ s-+:-+
## gender+f-m -0.042
## syllbl-cls+ 0.375 -0.019
## registr-h+l 0.083 0.004 -0.129
## gndr+f-m:s-+ -0.019 0.375 -0.025 -0.010
## gndr+f-m:r-+ 0.004 0.083 -0.010 -0.034 -0.129
## syllbl-+:-+ -0.011 -0.008 0.053 -0.191 0.035 0.002
## gnd+-:-+:-+ -0.008 -0.011 0.035 0.002 0.053 -0.191 -0.019
creaky <- glmer(creakiness ~ gender * syllable * register + (register |participant) , data = yng ,family = "binomial")
summary(creaky)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: creakiness ~ gender * syllable * register + (register | participant)
## Data: yng
##
## AIC BIC logLik deviance df.resid
## 898.0 954.3 -438.0 876.0 1223
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8573 -0.3811 -0.2928 -0.2220 6.1207
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 0.3242 0.5694
## register-h+l 0.2521 0.5021 0.52
## Number of obs: 1234, groups: participant, 23
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) -2.3061 0.1735 -13.289
## gender+f-m -0.2954 0.3326 -0.888
## syllable-closed+open 0.6412 0.2252 2.848
## register-h+l 0.5743 0.2733 2.101
## gender+f-m:syllable-closed+open 0.8496 0.4501 1.888
## gender+f-m:register-h+l -0.8437 0.5024 -1.679
## syllable-closed+open:register-h+l 1.1231 0.4503 2.494
## gender+f-m:syllable-closed+open:register-h+l 0.7238 0.9003 0.804
## Pr(>|z|)
## (Intercept) <2e-16 ***
## gender+f-m 0.3745
## syllable-closed+open 0.0044 **
## register-h+l 0.0356 *
## gender+f-m:syllable-closed+open 0.0590 .
## gender+f-m:register-h+l 0.0931 .
## syllable-closed+open:register-h+l 0.0126 *
## gender+f-m:syllable-closed+open:register-h+l 0.4214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr+- syll-+ rgst-+ gndr+f-m:s-+ gndr+f-m:r-+ s-+:-+
## gender+f-m 0.059
## syllbl-cls+ -0.350 -0.167
## registr-h+l 0.103 0.145 -0.165
## gndr+f-m:s-+ -0.165 -0.362 0.189 -0.156
## gndr+f-m:r-+ 0.148 0.175 -0.170 0.133 -0.168
## syllbl-+:-+ -0.130 -0.130 0.018 -0.446 0.252 -0.219
## gnd+-:-+:-+ -0.123 -0.129 0.253 -0.211 0.017 -0.478 0.189
summary(pairs(emmeans(creaky, ~ register | syllable ), adjust = "bonferroni"))
## NOTE: Results may be misleading due to involvement in interactions
## syllable = closed:
## contrast estimate SE df z.ratio p.value
## high - low -0.0127 0.425 Inf -0.030 0.9761
##
## syllable = open:
## contrast estimate SE df z.ratio p.value
## high - low -1.1358 0.266 Inf -4.278 <.0001
##
## Results are averaged over the levels of: gender
## Results are given on the log odds ratio (not the response) scale.
F1a <- yng[,c("vowel","F1_midpoint")]
colnames(F1a) <- c("vowel","F1")
F1b <- yng[,c("vowel","F1_0.75point")]
colnames(F1b) <- c("vowel","F1")
overalF1 <- rbind(F1a,F1b)
F2a <- yng[,c("vowel","F2_midpoint","syllable")]
colnames(F2a) <- c("vowel","F2","syllable")
F2b <- yng[,c("vowel","F2_0.75point","syllable")]
colnames(F2b) <- c("vowel","F2","syllable")
overalF2 <- rbind(F2a,F2b)
overalF2 <- overalF2[, -1]
#merge two data frames by vowel
overal <- cbind(overalF1, overalF2)
means <- overal %>%
group_by(vowel) %>%
summarise(mean_F1 = mean(F1),
mean_F2 = mean(F2))
ggplot(overal, aes(x = F2, y = F1, color = vowel, label = factor(vowel, levels = c("i","I","y","Y","e","round_e", "E","schwa", "A", "u", "ou", "o","unround_o","low_o","a"), labels = c("i","ɪʔ","y","ʏʔ","e","ø", "ɛ","əʔ", "ɐʔ", "ɯ", "u", "oʔ","ɤ","ɔ","ɑ")))) +
xlab("F2 (Hz)") +
ylab("F1 (Hz)") +
#ggtitle("Vowel space at the midpoint of glottal-ended vowels") +
geom_point(size = 0.95, alpha = 0.15) +
stat_ellipse(level = 0.50, na.rm = TRUE, lwd = 1.0) +
geom_text(data = means, aes(x = mean_F2, y = mean_F1, family="serif"), size=9) +
scale_x_reverse(limits = c(3000, 500)) + scale_y_reverse(limits = c(1200, 200)) +
scale_color_manual(values=c("#21918c", "#443983", "#31688e", "#90d743","#fde725","#443983","#443983","#90d743", "#440154","#440154", "#fde725","#35b779","#31688e","#35b779", "#21918c"
#"turquoise4","goldenrod","gold2","turquoise1","dodgerblue2", "darkorange","deepskyblue","royalblue","royalblue4"
# "#fde725", "#31688e", "#90d743", "#443983", "#35b779", "#440154", "#21918c"
)) +
guides(color = FALSE) +
theme_classic() +
theme(
axis.title.x = element_text(family = "serif", size = 12),
axis.title.y = element_text(family = "serif", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12))
## Warning: Removed 43 rows containing missing values (`geom_point()`).
ggplot(yng, aes(x=syllable, y=H1.H2, fill=register)) +
ylab("H1-H2 difference (dB/Hz)") +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.5, option="G") +
theme_ipsum() +
theme(
legend.position = "bottom",
#plot.title = element_text(family="Times", face="bold", size=12),
axis.title.x = element_text(family = "serif"),
axis.title.y = element_text(family = "serif", face="bold", size = 12),
axis.text.x = element_text(family = "serif", size = 12),
axis.text.y = element_text(family = "serif", size = 12),
legend.title = element_text(family = "serif", face="bold", size = 12),
legend.text = element_text(family = "serif", face="bold", size = 12)
) +
# ggtitle("Boxplot of the distribution of vowel duration") +
xlab("")
## Warning: Removed 13 rows containing non-finite values (`stat_boxplot()`).
median(yng$spectrum_difference, na.rm = T)
## [1] 0.02038421
quantile(yng$spectrum_difference, na.rm = T)
## 0% 25% 50% 75% 100%
## 0.00000000 0.01196686 0.02038421 0.05499226 0.45000000
upper.bound <- 0.05499226 + 1.5* (0.05499226 - 0.01196686)
lower.bound <- 0.01196686 - 1.5* (0.05499226 - 0.01196686)
quantile(yng$spectrum_difference, 0.95, na.rm = T)
## 95%
## 0.1844872
ggplot(yng, aes(x=spectrum_difference, na.rm = T)) +
geom_histogram(aes(y=..density..), # Histogram with density instead of count on y-axis
binwidth=.01,
colour="black", fill="white") +
geom_density(alpha=.2, fill="#FF6666") # Overlay with transparent density plot
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 10 rows containing non-finite values (`stat_bin()`).
## Warning: Removed 10 rows containing non-finite values (`stat_density()`).
yng <- yng[yng$spectrum_difference < 0.15, ]
breathy2 <- lmerTest::lmer(H1.H2 ~ gender * syllable * register + (syllable + register |participant), data = yng)
summary(breathy2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: H1.H2 ~ gender * syllable * register + (syllable + register |
## participant)
## Data: yng
##
## REML criterion at convergence: 7753.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3830 -0.5180 -0.0649 0.4241 5.5141
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## participant (Intercept) 17.452 4.178
## syllable-closed+open 4.113 2.028 0.87
## register-h+l 11.513 3.393 0.07 -0.21
## Residual 53.183 7.293
## Number of obs: 1127, groups: participant, 23
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -0.9467 0.9113 20.7568
## gender+f-m -2.2802 1.8225 20.7568
## syllable-closed+open 1.1800 0.6729 21.0835
## register-h+l -0.3464 0.8837 26.4083
## gender+f-m:syllable-closed+open 3.7119 1.3457 21.0835
## gender+f-m:register-h+l 0.5672 1.7674 26.4083
## syllable-closed+open:register-h+l 1.0394 1.0434 1072.1079
## gender+f-m:syllable-closed+open:register-h+l 1.6411 2.0869 1072.1079
## t value Pr(>|t|)
## (Intercept) -1.039 0.3108
## gender+f-m -1.251 0.2248
## syllable-closed+open 1.754 0.0940 .
## register-h+l -0.392 0.6982
## gender+f-m:syllable-closed+open 2.758 0.0118 *
## gender+f-m:register-h+l 0.321 0.7508
## syllable-closed+open:register-h+l 0.996 0.3194
## gender+f-m:syllable-closed+open:register-h+l 0.786 0.4318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gndr+- syll-+ rgst-+ gndr+f-m:s-+ gndr+f-m:r-+ s-+:-+
## gender+f-m -0.061
## syllbl-cls+ 0.404 0.031
## registr-h+l 0.053 0.013 -0.091
## gndr+f-m:s-+ 0.031 0.404 -0.165 -0.032
## gndr+f-m:r-+ 0.013 0.053 -0.032 -0.116 -0.091
## syllbl-+:-+ 0.009 -0.024 -0.017 -0.316 0.071 0.141
## gnd+-:-+:-+ -0.024 0.009 0.071 0.141 -0.017 -0.316 -0.244