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Speaker Adaptation Using Improved MAP Estimation with Small Amount of Adaptation Data

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Speaker Adaptation Using Improved MAP Estimation with Small Amount of Adaptation Data

IEEE TENCON 2013——In this study, we focus on the small vocabulary isolated word recognition system for a portable device such as the remote controller. MAP estimation is a well-known and reliable speaker adaptation technique based on Bayes theory. However, it is difficult to estimate the parameters without the corresponding adaptation data. In this report, we propose a method which solves this problem of MAP estimation. This method is an efficient approach for a small amount of adaptation data. It uses the similarity between states of acoustic models measured by Bhattacharyya distance, and estimates all parameters without the corresponding adaptation data. In experiments of a speaker dependent word recognition using a database consisting of 100 Japanese city names, the proposed method achieved 78.7% recognition accuracy compared to 77.8%of the conventional MAP estimation when 10 adaptation words were provided by target user.


关键词： MAP估计 贝叶斯理论 声学模型 小词汇量孤立词识别 IEEE TENCON 2013

主讲人：Takuya FUTAGAMI 机构：Osaka University

时长：0:13:15 年代：2013年

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