successive learning", respectively. These adaptation are evaluated using syllable perplexity and phrase recognition rates. The perplexity was reduced from 24.5 to 14.3 for the adaptation using 1000 phrases of similar text by preliminary learning, and was reduced to 12.1 using 1000 phrases including the 100 most recent phrases by successive learning. The recognition rates were also improved from 42.3% to 51.3% and 52.9%, respectively. Text similarity for the approaches is also studied in this paper." />


Task Adaptation in Syllable Trigram Models for Continuous Speech Recognition

Sho-ichi MATSUNAGA  Tomokazu YAMADA  Kiyohiro SHIKANO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E76-D   No.1   pp.38-43
Publication Date: 1993/01/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Speech and Discourse Processing in Dialogue Systems)
Category: 
Keyword: 
speech recognition,  stochastic language model,  

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Summary: 
In speech recognition systeme dealing with unlimited vocabulary and based on stochastic language models, when the target recognition task is changed, recognition performance decreases because the language model is no longer appropriate. This paper describes two approaches for adapting a specific/general syllable trigram model to a new task. One uses a amall amount of text data similar to the target task, and the other uses supervised learning using the most recent input phrases and similar text. In this paper, these adaptation methods are called preliminary learning" and successive learning", respectively. These adaptation are evaluated using syllable perplexity and phrase recognition rates. The perplexity was reduced from 24.5 to 14.3 for the adaptation using 1000 phrases of similar text by preliminary learning, and was reduced to 12.1 using 1000 phrases including the 100 most recent phrases by successive learning. The recognition rates were also improved from 42.3% to 51.3% and 52.9%, respectively. Text similarity for the approaches is also studied in this paper.