| Paper: | SP-L11.6 | ||
| Session: | Language Modeling and Search | ||
| Time: | Friday, May 21, 17:10 - 17:30 | ||
| Presentation: | Lecture | ||
| Topic: | Speech Processing: Language Modeling | ||
| Title: | THE USE OF A LINGUISTICALLY MOTIVATED LANGUAGE MODEL IN CONVERSATIONAL SPEECH RECOGNITION | ||
| Authors: | Wen Wang; SRI International / Purdue University | ||
| Andreas Stolcke; SRI International | |||
| Mary Harper; Purdue University | |||
| Abstract: | Structured language models have recently been shown to give significant improvements in large-vocabulary recognition relative to standard N-gram models, but typically imply a heavy computational burden and have not been applied to large training sets or complex recognition systems. In previous work, we developed a linguistically motivated and computationally efficient almost-parsing language model using a data structure derived from Constraint Dependency Grammar parses that tightly integrates knowledge of words, lexical features, and syntactic constraints. In this paper we show that such a model can be used effectively and efficiently in all stages of a complex, multi-pass conversational telephone speech recognition system. Compared to a state-of-the-art 4-gram interpolated word- and class-based language model, we obtained a 6.2\% relative word error reduction (a 1.6\% absolute reduction) on a recent NIST evaluation set. | ||
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