Latent topic modeling of word vicinity information for speech recognition

Kuan Yu Chen, Hsuan Sheng Chiu, Berlin Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages5394-5397
Number of pages4
DOIs
Publication statusPublished - 2010 Nov 8
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: 2010 Mar 142010 Mar 19

Publication series

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

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period10/3/1410/3/19

Keywords

  • Broadcast news transcription
  • Speech recognition
  • Topic language model
  • Word vicinity model

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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