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Barbertje
M. Streefkerk*, Louis C. W. Pols* and Louis F. M. ten Bosch**
*Institute
of Phonetic Sciences Amsterdam (IFA) / IFOTT
**Lernout
& Hauspie Speech Products N. V., Belgium
ABSTRACT
In
this paper we present several acoustical features, which are used as predictors
for prominence. A set of 1244 sentences from 273 different speakers is selected
from the Dutch Polyphone Corpus. Via listening experiments the subjective
prominence markers are obtained. Several acoustical features concerning F
0,
energy and duration are derived and used as predictors for prominence. The
sentences are divided in a test and a training set, to test and train neural
networks with different topologies and different input features. The first
results show that a classification of prominent and non-prominent words is
possible with 82.1% correct for an independent test set.