Ruiqiang Zhang, Ezra Black, Andrew Finch, Yoshinori Sagisaka
Using Detailed Contextual
Information To Build Language
Models Of Part-Of-Speech Tagging
And Language Models Of Speech
Recognition By The Maximum
Entropy Approach
Abstract:This report is about us the latest results of part-of-speech tagging and language modeling of speech recognition. Detailed information including local N-gram,
long distance constraints and information from sentence structure provided by ATR
Parser are integrated in language models by maximum entropy approach. The experimental results prove our models are effective to improve pos tagging accuracy
and reduce word error rate of ATR speech recognition system — ATRSPREC.