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INTERSPEECH
2010

Improvements of search error risk minimization in viterbi beam search for speech recognition

12 years 10 months ago
Improvements of search error risk minimization in viterbi beam search for speech recognition
This paper describes improvements in a search error risk minimization approach to fast beam search for speech recognition. In our previous work, we proposed this approach to reduce search errors by optimizing the pruning criterion. While conventional methods use heuristic criteria to prune hypotheses, our proposed method employs a pruning function that makes a more precise decision using rich features extracted from each hypothesis. The parameters of the function can be estimated to minimize a loss function based on the search error risk. In this paper, we improve this method by introducing a modified loss function, arc-averaged risk, which potentially has a higher correlation with actual error rate than the original one. We also investigate various combinations of features. Experimental results show that further search error reduction over the original method is obtained in a 100K-word vocabulary lecture speech transcription task.
Takaaki Hori, Shinji Watanabe, Atsushi Nakamura
Added 18 May 2011
Updated 18 May 2011
Type Journal
Year 2010
Where INTERSPEECH
Authors Takaaki Hori, Shinji Watanabe, Atsushi Nakamura
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