This paper deals with automatic sentence boundary detection in spoken Czech using both textual and prosodic information. This task is important to make automatic speech recognition (ASR) output more readable and easier for downstream language processing modules. We compare and combine three statistical models – hidden Markov model, maximum entropy, and adaptive boosting. We evaluate these methods on two Czech corpora, broadcast news and broadcast conversations, using both manual and ASR transcripts. Our results show that superior results are achieved when all the three models are combined via posterior probability interpolation, and that there is substantial difference among the three methods when using different knowledge sources, as well as in different genres. Feature analysis also reveals significant differences in prosodic feature usage patterns between the two genres.
Index Terms: sentence segmentation, prosody, HMM, maximum entropy, boosting
Cite as: Kolář, J., Liu, Y. (2010) Comparing and combining modeling techniques for sentence segmentation of spoken Czech using textual and prosodic information. Proc. Speech Prosody 2010, paper 021
@inproceedings{kolar10_speechprosody,
author={Jáchym Kolář and Yang Liu},
title={{Comparing and combining modeling techniques for sentence segmentation of spoken Czech using textual and prosodic information}},
year=2010,
booktitle={Proc. Speech Prosody 2010},
pages={paper 021}
}