Technical Program

Paper Detail

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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