Latent topic modeling of word vicinity information for speech recognition

Kuan Yu Chen*, Hsuan Sheng Chiu, Berlin Chen

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

13 引文 斯高帕斯(Scopus)

摘要

Topic language models, mostly revolving around the discovery of "word-document" co-occurrence dependence, have attracted significant attention and shown good performance in a wide variety of speech recognition tasks over the years. In this paper, a new topic language model, named word vicinity model (WVM), is proposed to explore the co-occurrence relationship between words, as well as the long-span latent topical information for language model adaptation. A search history is modeled as a composite WVM model for predicting a decoded word. The underlying characteristics and different kinds of model structures are extensively investigated, while the performance of WVM is thoroughly analyzed and verified by comparison with a few existing topic language models. Moreover, we also present a new modeling approach to our recently proposed word topic model (WTM), and design an efficient way to simultaneously extract "word-document" and "word-word" co-occurrence characteristics through the sharing of the same set of latent topics. Experiments on broadcast news transcription seem to demonstrate the utility of the presented models.

原文英語
主出版物標題2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
頁面5394-5397
頁數4
DOIs
出版狀態已發佈 - 2010
事件2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, 美国
持續時間: 2010 三月 142010 三月 19

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(列印)1520-6149

其他

其他2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
國家/地區美国
城市Dallas, TX
期間2010/03/142010/03/19

ASJC Scopus subject areas

  • 軟體
  • 訊號處理
  • 電氣與電子工程

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