In order to get more consistent representations, we will first of all
distinguish long vs. short vowels (in TIMITBET notation):
short: /iy, ih, eh, ix, ax, ah, uw, uh/
long: /ae, aa, ao, ey, ay, oy, aw, ow, er/
The main effects of short vs. long vowels, stress and utterance-final
lengthening are summarised in Table 1 together with data from Crystal &
House . All three effects (stress, vowel length, utterance position) are
tangent and are potentially useful to improve automatic speech recognition
TIMIT C. & H. TIMIT C. & H.
short V short long V long V
(8) V (4) (9) (7)
ms n ms n ms n ms n
uns 60 13,9 56 842 93 3,55 84 286
str 87 14,1 93 601 133 13,8 15 1,4
66 91 1 11
uf 78 1,19 81 39 109 408 11 7
uns 9 0
uf 14 954 14 78 177 1,13 20 125
str 2 7 5 2
unf 59 12,7 56 727 90 3,14 77 224
uns 66 2
unf 83 13,2 85 253 129 12,7 13 628
str 12 56 4
Table 1: Mean vowel duration in ms and number of instances (n) for TIMIT and
data of Crystal & House (C. & H.), in stressed (str) and unstressed
(uns) syllables and in utterance final (uf) and non-final (unf)
Table 2 presents similar subdivisions of the vowel data separated for
word-final vs. non-final, whereas the duration mean for vowels in mono-syllabic
words is given separately. The distinction between stressed and unstressed is
still very apparent.
short vowel long vowel
unstres stressed unstress stressed
ms n ms n ms n ms n
wf 71 5022 113 1268 97 1583 148 175
nwf 55 5858 82 4728 89 1679 123 540
mono 52 3085 86 8170 86 288 138 673
Table 2: Mean vowel duration of TIMIT data set for word-final (wf),
non-word-final (nwf) and monosyllable words (mono).
However, the effect of the position within the word is not very consistent
at all, perhaps apart from the somewhat longer duration for short vowels in
Most phonetic handbooks tell us that in languages like English (e.g. )
and Dutch  a vowel preceding a voiced plosive is generally longer than the
same vowel preceding an unvoiced plosive, and actually the reverse seems to be
true for the closure time. Also in rule-based synthesis these vowel- and
closure-duration features are successfully applied to improve quality and
intelligibility . Van Santen  presents very nice data for
word-penultimate stressed vowel duration as a function of the post-vocalic
consonant for a single male speaker in a 71-minutes database of read sentences
(containing 2,162 sentences, 13,048 words, and 18,046 vowel segments).
Differences in average vowel duration of about 100 ms were found for voiced vs.
voiceless plosives. We wondered whether similarly consistent effects could be
found for a much less homogeneous database of many different speakers such as
Similar to Van Santen, we limited ourselves to stressed vowels only. It was
apparent that for the present database the distributions of vowels followed by
voiced and unvoiced plosives are very similar indeed! Only the tails of the
distributions give some indication of a lengthening effect for voiced plosives.
So far hardly any ASR system takes the systematic effect of speaking rate
into account. In the 1994 Benchmark test for the ARPA Spoken Language Program
clear evidence was presented  that speakers with a high speaking rate (in
terms of number of words per minute) almost unanimously showed a higher word
error rate for all 20 systems that participated in the so-called baseline Hub1
C1 test. This certainly makes it interesting to see whether rate-specific
duration information could help in improving recognition performance. Jones
& Woodland  already showed that speaking rate can be measured at
recognition time and that it can be used to improve the speech recognition
behaviour through e.g. post processing.
The normalised phone duration  is chosen as the basic unit, this way
we correct for the intrinsically long or short duration of specific phones. The
relative speaking rate of an utterance then is defined as the average
normalised phone duration in that utterance. Actually this is more like the
reciprocal of rate, since the higher that number the slower the rate. Fig. 2
gives a histogram distribution of the relative speaking rate for all 3,696
utterances. It is similar to the usual duration pdf of most phones, having a
binomial-like distribution. For comparison, the utterance-averaged absolute
phone durations in the corresponding histogram bins are also shown. It can be
seen that the averaged absolute phone duration has a near-linear relation with
the relative utterance speaking rate, this is particularly true in the middle
region, where counts are large. The irregularities in the periphery are due to
the fact that these represent relatively few utterances for which the intrinsic
phone duration may vary a lot. We divided all sentences into the three
categories fast, medium and slow, and derived phone
duration distributions accordingly.
Figure 2: Histogram of the relative speaking rate for the whole data set. The
dark curve represents the utterance-averaged phone duration in ms (against
right axis) in each histogram bin.
The duration statistics derived in the preceding section, were all based on
the hand-labelled TIMIT material. This could be the basis for an initialisation
process to add duration-specific knowledge to a recognition system. However,
the HMM-based recogniser will never be able to recognise and reproduce all
these phone segments with their correct duration. So, our next step is to see
how close the phone durations after Viterbi search match the hand-labelled
segments. If one plots the duration of the manually labelled segments against
that of the segments after Viterbi search (not shown) it can be seen that the
number of outliers can be quite serious. The TIMIT database has been used
repeatedly to test automatic segmentation procedures (e.g. ) with comparable
results. About 15% of the phone duration, with full knowledge of the phone
sequence, deviates more than 20 ms from the actual duration. For all 50 phones
together this is a rather even distribution around the diagonal, however for
individual phones the deviation can be much more uneven. In any initial
training based on the hand-labelled data, such deviations cannot be taken into
account and perhaps should be taken care of in some later phase.
By studying one effect after the other, as done above, a much better view
on the significance of various parameters is obtained. However, a quantitative
comparison is not very well possible along those lines. Inspired by Sun &
Deng  who analysed the spectral variability using TIMIT, we have developed
a hierarchically structured Analysis of Variance (ANOVA), that in principle
should make it possible to analyse the contribution of various identifiable
factors to the overall durational variability in the TIMIT database.
There is no straightforward ANOVA that can solve this problem, the
complications lie at various levels:
* the inability to model this complex factorial design in a fully satisfactory
* the sheer size of the data, which leads to memory problems;
* the problem of nested factors;
* empty cells and singletons;
* the ordering problem.
We intend to analyse the variation in phone duration as explained by the
following 11 factors:
R speaking rate L syllable location
u in utterance
C broad phonetic G gender of speaker
P phone D dialect region of
h r speaker
P phone in context S speaker
S stress S phone segment
w location in word
Each factor has a different number of levels and some of these numbers have
complicated relations with others. The relations between all the levels in all
the 11 factors can be shown in a tree, part of which is represented in Fig.
The result of calculating the sum-of-squares (SS) terms in
percentages in each of the subsequent 11 factors is shown in Table 3 for two
different orderings of the factors. The most tangent phenomenon seen from these
percentages is, that when Sp (and G and Dr) are put before
Cl and Ph (see lower section of Table 3), the variation in
Sp is rather small, while when Sp (and G and Dr)
are put after the splitting of the data by Cl and Ph, Sp
explains a much larger percentage of variation.
R Cl Ph Pt S Lw Lu G Dr Sp Sg los
2. 15 26 0. 0. 0. 0. 0. 2. 16 34 0.8
3 .1 .0 2 4 8 9 3 2 .0 .9
R G Dr Sp Cl Ph Pt S Lw Lu Sg los
2. 0. 0. 0. 19 37 1. 1. 1. 0. 34 0.7
3 0 0 6 .6 .5 1 2 5 6 .9
Table 3: Percentages of variations in terms of SS in the 11 factors calculated
from the whole data set. The upper and lower sections show two different
ordering of the factors. The last column shows the loss of calculated SS due to
For the moment we suppose that the upper row of percentage numbers given in
Table 3, properly reflects the importance of various factors, in terms of
variation explained. However, this puts us in a somewhat uneasy position, since
several factors that appeared to have a consistent effect in the distributions
presented in sect. 2, such as speaking rate R and stress S, here
are only responsible for 2.3% and 0.4%, respectively! A possible explanation
for these discrepancies is that any ANOVA can only present overall effects per
factor, whereas for instance in Table 1 the effect of stress is presented for
long and short vowels separately. In a regular ANOVA such effects show up in
interaction terms, which are not available in the present analysis.
Figure 3: Part of the tree for ANOVA for the 11 indicated factors. Also the
number of possible splits of levels per factor, as well as the cumulative
numbers of levels, are indicated.
It strikes us that so many of the very consistent facts that Van Santen
 could demonstrate in his large speech database for a single male speaker
did not show up in the multi-speaker TIMIT database. It made us aware once
again that `real speech', although still read from paper, and `non-professional
speakers' under semi-controlled conditions introduce a lot of variation. As a
consequence, only certain duration features may be beneficial for improving
recognition performance. We will continue that search, and we hope that the
methodology that we have applied so far, may show to be productive for other
specific knowledge sources, such as pitch, as well. Meanwhile, in our
research project about duration modelling, we build duration models based on a
subset of all the contextual factors discussed in this work, and integrate them
into the post-processing part of the recogniser using N-best sentence
alternatives . A more extended version of this paper will be published in
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