Pseudo-Syntactic Language Modeling for Disfluent Speech Recognition
| Type of publication: | Idiap-RR |
| Citation: | McGreevy04a |
| Number: | Idiap-RR-55-2004 |
| Year: | 2004 |
| Institution: | IDIAP |
| Note: | Published in Proceedings of SST, 2004 |
| Abstract: | 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. |
| Userfields: | ipdmembership={speech}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Crossref by |
McGreevy04b |
| Added by: | [UNK] |
| Total mark: | 0 |
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