Topic tracking language model for speech recognition

In a real environment, acoustic and language features often vary depending on the speakers, speaking styles and topic changes. To accommodate these changes, speech recognition approaches that include the incremental tracking of changing environments have attracted attention. This paper proposes a to...

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Bibliographic Details
Published inComputer speech & language Vol. 25; no. 2; pp. 440 - 461
Main Authors Watanabe, Shinji, Iwata, Tomoharu, Hori, Takaaki, Sako, Atsushi, Ariki, Yasuo
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier Ltd 01.04.2011
Elsevier
Subjects
Applied linguistics
C (programming language)
Changing environments
Computational linguistics
Language model
Latent topic model
Linguistics
Mathematical models
On-line algorithm
On-line systems
Speech
Speech recognition
Texts
Topic tracking
Tracking
Topic tracking
Language model
Latent topic model
On-line algorithm
Speech recognition
Acoustics
Computational linguistics
Speech processing
Algorithm
Japanese
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Summary:In a real environment, acoustic and language features often vary depending on the speakers, speaking styles and topic changes. To accommodate these changes, speech recognition approaches that include the incremental tracking of changing environments have attracted attention. This paper proposes a topic tracking language model that can adaptively track changes in topics based on current text information and previously estimated topic models in an on-line manner. The proposed model is applied to language model adaptation in speech recognition. We use the MIT OpenCourseWare corpus and Corpus of Spontaneous Japanese in speech recognition experiments, and show the effectiveness of the proposed method.
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ISSN:0885-2308
1095-8363
DOI:10.1016/j.csl.2010.07.006