模式识别与人工智能
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  2010, Vol. 23 Issue (2): 165-170    DOI:
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An Automatic Language Identification Method Based on Supervector Subspace Analysis
SONG Yan,DAI Li-Rong,WANG Ren-Hua
Department of Electronic Engineering and Information Science,University of Science Technology of China,HeFei 230027

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Abstract  In automatic spoken language identification on telephone conversation speech, the interference caused by different signal path, different speech content or different speakers is a major factor affecting the performance. To tackle this problem, a supervector subspace analysis based automatic language identification method is proposed. In this method the supervector is first introduced to represent the training utterances. Then a discriminative training method is applied based on SVM model. Furthermore the subspace analysis is utilized to estimate the noise subspace. Finally the noise is subtracted from the distance metric. The experiments on NIST 07 30 and 10 sec evaluation task show the advantage of this method and compared with baseline system the performance is increased clearly, the EER(Equal Error Rate) being reduced relatively about 20%.
Key wordsLanguage Identification      Discriminative Training      Subspace Analysis     
Received: 25 November 2008     
ZTFLH: TN911.72  
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SONG Yan
DAI Li-Rong
WANG Ren-Hua
Cite this article:   
SONG Yan,DAI Li-Rong,WANG Ren-Hua. An Automatic Language Identification Method Based on Supervector Subspace Analysis[J]. , 2010, 23(2): 165-170.
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http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2010/V23/I2/165
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