11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Enhanced Word Classing for Model M

Stanley F. Chen, Stephen M. Chu

IBM T.J. Watson Research Center, USA

Model M is a superior class-based n-gram model that has shown improvements on a variety of tasks and domains. In previous work with Model M, bigram mutual information clustering has been used to derive word classes. In this paper, we introduce a new word classing method designed to closely match with Model M. The proposed classing technique achieves gains in speech recognition word-error rate of up to 1.1% absolute over the baseline clustering, and a total gain of up to 3.0% absolute over a Katz-smoothed trigram model, the largest such gain ever reported for a class-based language model.

Full Paper

Bibliographic reference.  Chen, Stanley F. / Chu, Stephen M. (2010): "Enhanced word classing for model M", In INTERSPEECH-2010, 1037-1040.