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.
Cite as: Streefkerk, B.M., Pols, L.C.W., Bosch, L.F.M.t. (1999) Acoustical features as predictors for prominence in read aloud dutch sentences used in ANN's. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 551-554, doi: 10.21437/Eurospeech.1999-142
@inproceedings{streefkerk99_eurospeech,
author={Barbertje M. Streefkerk and Louis C. W. Pols and Louis F. M. ten Bosch},
title={{Acoustical features as predictors for prominence in read aloud dutch sentences used in ANN's}},
year=1999,
booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
pages={551--554},
doi={10.21437/Eurospeech.1999-142}
}