In the current study, we propose and evaluate a new method for automatic intonational phrase break prediction based on sequences of parts-of-speech and word junctures. The proposed method uses decision trees to estimate the probability of a word juncture type (break or nonbreak) given a finite length window of part-of-speech values, and uses an n-gram to model the word juncture sequence. Trained on an 8,000 word database, our algorithm predicted breaks with F=77% and nonbreaks with F=93%, which represents a significant improvement over the commonly used approach, which uses decision trees alone.
Cite as: Sun, X., Applebaum, T.H. (2001) Intonational phrase break prediction using decision tree and n-gram model. Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001), 537-540, doi: 10.21437/Eurospeech.2001-144
@inproceedings{sun01_eurospeech,
author={Xuejing Sun and Ted H. Applebaum},
title={{Intonational phrase break prediction using decision tree and n-gram model}},
year=2001,
booktitle={Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001)},
pages={537--540},
doi={10.21437/Eurospeech.2001-144}
}