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Topic tracking language model for speech recognition
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DOI：10.1016/j.csl.2010.07.006.png)
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Abstract
En 中文
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. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Language model
Latent topic model
Topic tracking
On-line algorithm
Speech recognition
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