Sixth European Conference on Speech Communication and Technology
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.
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Bibliographic reference. Streefkerk, Barbertje M. / Pols, Louis C. W. / Bosch, Louis F. M. ten (1999): "Acoustical features as predictors for prominence in read aloud dutch sentences used in ANN's", In EUROSPEECH'99, 551-554.