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%.
宋彦，戴礼荣，王仁华. 基于超向量子空间分析的自动语种识别方法[J]. 模式识别与人工智能, 2010, 23(2): 165-170.
SONG Yan,DAI Li-Rong,WANG Ren-Hua. An Automatic Language Identification Method Based on Supervector Subspace Analysis. , 2010, 23(2): 165-170.
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2010, Vol. 23 
