Parse structure and segmentation for improving speech recognition

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 qua...

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Bibliographic Details
Published in:	2006 IEEE Spoken Language Technology Workshop pp. 90 - 93
Main Authors:	McNeill, W.P., Kahn, J.G., Hillard, D.L., Ostendorf, M.
Format:	Conference Proceeding
Language:	English
Published:	01-12-2006
Subjects:	Broadcasting Degradation Error analysis Feature extraction Measurement standards Natural languages Performance gain Speech analysis Speech recognition Testing
Online Access:	Get full text
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Summary:	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.
ISBN:	1424408725 9781424408726
DOI:	10.1109/SLT.2006.326824