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Topic-Independent Speaking-Style Transformation of Language Model for Spontaneous Speech Recognition

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2 Author(s)
Yuya Akita ; Academic Center for Computing and Media Studies, Kyoto University, Kyoto 606-8501, Japan ; Tatsuya Kawahara

For language modeling of spontaneous speech, we propose a novel approach, based on the statistical machine translation framework, which transforms a document-style model to the spoken style. For better coverage and more reliable estimation, incorporation of POS (part-of-speech) information is explored in addition to lexical information. In this paper, we investigate several methods that combine POS-based model or integrate POS information in the ME (maximum entropy) scheme. They achieve significant reduction in perplexity and WER in a meeting transcription task. Moreover, the model is applied to different domains or committee meetings of different topics. As a result, even larger perplexity reduction is achieved compared with the case tested in the same domain. The result demonstrates the generality and portability of the model.

Published in:

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07  (Volume:4 )

Date of Conference:

15-20 April 2007