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PERFORMANCE IMPROVEMENT OF AUTOMATIC SPEECH RECOGNITION SYSTEMS VIA MULTIPLE LANGUAGE MODELS PRODUCED BY SENTENCE-BASED CLUSTERING

Sushil Kumar PodderKhaled ShabanJiping SunFakhri KarrayOtman BasirMohamed Kamel

PAMI Lab,University of Waterloo,Waterloo,CANADA

摘要:<正>Grammar-based speech recognition systems exhibit performance degradation as their vocabulary sizes increase.Data clustering is deemed to reduce the proportionality of this problem.We introduce an approach to data clustering for automatic speech recognition systems using Kohonen Self-Organized Map.Clustering results are used further to build a language model for each of the clusters using CMU-Cambridge toolkit.The approach was implemented as a prototype for a large vocabulary and continuous speech recognition system and about 8% performance improvement was achieved in comparison with the performance achieved using the language model and dictionary provided by Sphinx3. In this paper we present the experimental results along with discussions,analysis and potential future directions.
会议名称:

2003 International Conference on Natural Language Processing and Knowledge Engineering

会议时间:

2003-10-26

会议地点:

中国北京

  • 专辑:

    信息科技

  • 专题:

    电信技术

  • 分类号:

    TN912.34

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