Open Access

Automatic Query Generation and Query Relevance Measurement for Unsupervised Language Model Adaptation of Speech Recognition

  • Akinori Ito1Email author,
  • Yasutomo Kajiura1,
  • Motoyuki Suzuki2 and
  • Shozo Makino1
EURASIP Journal on Audio, Speech, and Music Processing20092009:140575

DOI: 10.1155/2009/140575

Received: 3 December 2008

Accepted: 25 October 2009

Published: 14 December 2009


We are developing a method of Web-based unsupervised language model adaptation for recognition of spoken documents. The proposed method chooses keywords from the preliminary recognition result and retrieves Web documents using the chosen keywords. A problem is that the selected keywords tend to contain misrecognized words. The proposed method introduces two new ideas for avoiding the effects of keywords derived from misrecognized words. The first idea is to compose multiple queries from selected keyword candidates so that the misrecognized words and correct words do not fall into one query. The second idea is that the number of Web documents downloaded for each query is determined according to the "query relevance." Combining these two ideas, we can alleviate bad effect of misrecognized keywords by decreasing the number of downloaded Web documents from queries that contain misrecognized keywords. Finally, we examine a method of determining the number of iterative adaptations based on the recognition likelihood. Experiments have shown that the proposed stopping criterion can determine almost the optimum number of iterations. In the final experiment, the word accuracy without adaptation (55.29%) was improved to 60.38%, which was 1.13 point better than the result of the conventional unsupervised adaptation method (59.25%).

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Authors’ Affiliations

Graduate School of Engineering, Tohoku University
Institute of Technology and Science, University of Tokushima 2-1


© Akinori Ito et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.