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Parse structure and segmentation for improving speech recognition

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4 Author(s)
McNeill, W.P. ; Dept. of Linguistics, Washington Univ., Seattle, WA ; Kahn, J.G. ; Hillard, D.L. ; Ostendorf, M.

Separate avenues of prior work have shown that parsing language models lead to improved recognition performance, and that segmentation of speech into sentence-like units has an impact on parser performance. This paper brings these two findings together, showing that segmentation also impacts the quality of a syntax-based language model, such that larger reductions in word error rate are possible when using sentence-like segmentations rather than simple paused-based strategies. Further, we show that the same types of syntactic features used in parse reranking can also be used to reduce word error rate in an N-best rescoring framework.

Published in:

Spoken Language Technology Workshop, 2006. IEEE

Date of Conference:

10-13 Dec. 2006