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  4. Pseudo-Syntactic Language Modeling for Disfluent Speech Recognition
 
conference paper

Pseudo-Syntactic Language Modeling for Disfluent Speech Recognition

McGreevy, Michael
2004
Proceedings of SST 2004 (10th Australian International Conference on Speech Science & Technology), Sydney, Australia, 2004
Proceedings of SST 2004 (10th Australian International Conference on Speech Science & Technology), Sydney, Australia, 2004

Language models for speech recognition are generally trained on text corpora. Since these corpora do not contain the disfluencies found in natural speech, there is a train/test mismatch when these models are applied to conversational speech. In this work we investigate a language model (LM) designed to model these disfluencies as a syntactic process. By modeling self-corrections we obtain an improvement over our baseline syntactic model. We also obtain a 30% relative reduction in perplexity from the best performing standard {N-gram} model when we interpolate it with our syntactically derived models.

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