Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
October 16-20, 2000

Error Recovery and Sentence Verification Using Statistical Partial Pattern Tree for Conversational Speech

Chung-Hsien Wu, Yeou-Jiunn Chen, Cher-Yao Yang

Department of Computer Science and Engineering, Cheng Kung University, Tainan, Taiwan

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.

Full Paper

Bibliographic reference.  Wu, Chung-Hsien / Chen, Yeou-Jiunn / Yang, Cher-Yao (2000): "Error recovery and sentence verification using statistical partial pattern tree for conversational speech", In ICSLP-2000, vol.4, 624-627.