Topic cache language model for speech recognition

Chuang Hua Chueh*, Jen-Tzung Chien

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Traditional n-gram language models suffer from insufficient long-distance information. The cache language model, which captures the dynamics of word occurrences in a cache, is feasible to compensate this weakness. This paper presents a new topic cache model for speech recognition based on the latent Dirichlet language model where the latent topic structure is explored from n-gram events and employed for word prediction. In particular, the long-distance topic information is continuously updated from the large-span historical words and dynamically incorporated in generating the topic mixtures through Bayesian learning. The topic cache language model does effectively characterize the unseen n-gram events and catch the topic cache for long-distance language modeling. In the experiments on Wall Street Journal corpus, the proposed method achieves better performance than baseline n-gram and the other related language models in terms of perplexity and recognition accuracy.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages5194-5197
Number of pages4
DOIs
StatePublished - 8 Nov 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: 14 Mar 201019 Mar 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period14/03/1019/03/10

Keywords

  • Bayes procedure
  • Clustering method
  • Natural language
  • Smoothing method
  • Speech recognition

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