Statistical method of building dialect language models for ASR systems

Naoki Hirayama, Shinsuke Mori, Hiroshi G. Okuno

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

This paper develops a new statistical method of building language models (LMs) of Japanese dialects for automatic speech recognition (ASR). One possible application is to recognize a variety of utterances in our daily lives. The most crucial problem in training language models for dialects is the shortage of linguistic corpora in dialects. Our solution is to transform linguistic corpora into dialects at a level of pronunciations of words. We develop phonemesequence transducers based on weighted finite-state transducers (WFSTs). Each word in common language (CL) corpora is automatically labelled as dialect word pronunciations. For example, anta (Kansai dialect) is labelled anata (the most common representation of 'you' in Japanese). Phoneme-sequence transducers are trained from parallel corpora of a dialect and CL. We evaluate the word recognition accuracy of our ASR system. Our method outperforms the ASR system with LMs trained from untransformed corpora in written language by 9.9 points.

本文言語English
ホスト出版物のタイトル24th International Conference on Computational Linguistics - Proceedings of COLING 2012: Technical Papers
ページ1179-1194
ページ数16
出版ステータスPublished - 2012
外部発表はい
イベント24th International Conference on Computational Linguistics, COLING 2012 - Mumbai
継続期間: 2012 12 82012 12 15

Other

Other24th International Conference on Computational Linguistics, COLING 2012
CityMumbai
Period12/12/812/12/15

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Language and Linguistics
  • Linguistics and Language

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