Task adaptation in stochastic language models for continuous speech recognition

Abstract

The authors describe two approaches for adapting a specific syllable trigram model to a new task. One uses a small amount of text data similar to the target task, and the other uses supervised learning using the most recent input phrases. The effect of each adaptation is verified with syllable perplexity and phrase recognition. Where the syntactic knowledge was only the syllable trigram model, the perplexity was reduced from 54.5 to 18.1 for the adaptation using 100 phrases of similar text, and was reduced to 14.6 by the supervised learning. The recognition rates were also improved from 42.3% to 46.6% and 50.9%, respectively. Text similarity for speech recognition is also studied.< >