Incorporating proximity information for relevance language modeling in speech recognition

Yi Wen Chen, Bo Han Hao, Kuan Yu Chen, Berlin Chen

研究成果: 雜誌貢獻會議論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

Language modeling (LM), aiming to provide a statistical mechanism to associate quantitative scores to sequences of words, has long been an interesting yet challenging problem in the field of speech and language processing. Although the ngram model remains the predominant one, a number of disparate LM methods have been developed to complement the n-gram model. Among them, relevance modeling (RM) that explores the relevance information inherent between the search history and an upcoming word has shown preliminary promise for dynamic language model adaptation. This paper continues this general line of research in two significant aspects. First, the so-called "bag-of- words" assumption of RM is relaxed by incorporating word proximity evidence into the RM formulation. Second, latent topic information is additionally explored in the hope to further enhance the proximity-based RM framework. A series of experiments conducted on a large vocabulary continuous speech recognition (LVCSR) task seem to demonstrate that the various language models deduced from our framework are very comparable to existing language models.

原文英語
頁(從 - 到)2683-2687
頁數5
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版狀態已發佈 - 2013 一月 1
事件14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013 - Lyon, 法国
持續時間: 2013 八月 252013 八月 29

ASJC Scopus subject areas

  • 語言與語言學
  • 人機介面
  • 訊號處理
  • 軟體
  • 建模與模擬

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