K-L Divergence Based Model Clustering Method for Fast Speaker Identification
WANG Huan-Liang1,2,HAN Ji-Qing1,ZHENG Gui-Bin1
1.School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001 2.College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266035
Abstract：With the increase of enrolled speakers and audio data to be recognized, the conventional speaker identification methods can not meet the real-time demand for internet application environment. A K-L divergence based speaker model clustering method is proposed to construct a hierarchical identification system, which remarkably improves the recognition efficiency. Moreover, the confidence measure using class-level identification information is also investigated to effectively exclude out-of-set speaker as early as possible. The experimental results show the proposed method averagely increases the identification speed by 3.2 times while the error rate of closed-set identification only increases about 0.9% compared with the conventional method. The open-set identification can be speeded up by using class-level confidence measure and a relatively 5.1% error rate reduction can be achieved on out-of-set speakers identification while keeping the identification performance of in-set speakers unchanged.
王欢良，韩纪庆，郑贵滨. 基于K-L散度模型聚类的快速说话人辨识方法[J]. 模式识别与人工智能, 2010, 23(6): 856-861.
WANG Huan-Liang,HAN Ji-Qing,ZHENG Gui-Bin. K-L Divergence Based Model Clustering Method for Fast Speaker Identification. , 2010, 23(6): 856-861.
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2010, Vol. 23 
