基于K-L散度模型聚类的快速说话人辨识方法  

K-L Divergence Based Model Clustering Method for Fast Speaker Identification

作　　者：王欢良[1,2] 韩纪庆[1] 郑贵滨[1] 

机构地区：[1]哈尔滨工业大学计算机科学与技术学院,哈尔滨150001 [2]青岛科技大学信息科学与技术学院,青岛266035

出　　处：《模式识别与人工智能》2010年第6期856-861,共6页Pattern Recognition and Artificial Intelligence

基　　金：国家973计划项目(No.2007CB311100);国家863计划重点项目(No.2006AA010103)资助

摘　　要：在网络应用环境下,需要处理的音频数据和注册说话人急剧增加,传统说话人辨识方法难以满足实时性要求.文中提出采用K-L散度的说话人模型聚类方法,从而构造一个分级辨识模型,提高辨识效率.研究利用类辨识信息估计置信度的方法,可尽早有效排除集外说话人.实验结果显示,文中方法可使辨识速度平均提高3.2倍,而闭集辨识错误率平均只有0.9%的增加.采用类辨识置信度进一步提高开集辨识速度,并且在保持集内错误率不变的情况下,使集外错误率相对下降5.1%.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.

关 键 词：K-L散度 模型聚类 置信度 说话人辨识 网络环境 

分 类 号：TN912.34[电子电信—通信与信息系统]