PARSE STRUCTURE AND SEGMENTATION FOR IMPROVING SPEECH RECOGNITION
William H. McNeill, Jeremy M. Kahn, Dustin Hillard, Mari Ostendorf · 2006
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.