In this paper, in order to deal with the problems of disfluencies in conversational speech, partial pattern tree (PPT) and a PPT-based statistical language model are proposed. A partial pattern is defined to represent a sub-sentence with a key-phrase and some optional/functional phrases. The PPT is an integrated tree structure of the partial patterns generated from the training sentences and used to model the n-gram and grammatical constraints. In addition, a PPT merging algorithm is also proposed to reduce the number of partial patterns with similar syntactic structure by minimizing an objective cost function. Using the PPT, the undetected/misdetected errors due to disfluencies can be recovered. Finally, a sentence verification approach is proposed to re-rank the recovered sentences generated from the PPT. In order to assess the performance, a faculty name inquiry system with 2583 names has been implemented. The recognition acculacy of the system using the proposed PPT achieved 77.2%. We also contrasted this method with previous conventional approaches to show its superior performance.
Cite as: Wu, C.-H., Chen, Y.-J., Yang, C.-Y. (2000) Error recovery and sentence verification using statistical partial pattern tree for conversational speech. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 4, 624-627, doi: 10.21437/ICSLP.2000-889
@inproceedings{wu00j_icslp,
author={Chung-Hsien Wu and Yeou-Jiunn Chen and Cher-Yao Yang},
title={{Error recovery and sentence verification using statistical partial pattern tree for conversational speech}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 4, 624-627},
doi={10.21437/ICSLP.2000-889}
}