摘要： 研究基于微聚类算法的VoIP压缩码流说话人识别算法。给出直接从G.729, G.723.1(6.3 Kb/s), G.723.1(5.3 Kb/s)压缩语音的码流中提取识别参数，以微聚类算法作为识别结构的说话人识别算法。实验结果表明，对比在压缩码流中使用同样识别参数的GMM模型，微聚类算法在识别正确率和效率上都有很大的提高。

关键词: 说话人识别, 微聚类, 压缩参数

Abstract: This paper presents compressed speaker recognition approach for VoIP(Voice over IP) which is based on the micro-clustering algorithm. It designs a framework based on the micro-clustering algorithm and performs speaker recognition on the feature vector which is directly extracted from G.729, G723.1(6.3 Kb/s), G723.1(5.3 Kb/s) compressed stream. Experimental result shows that the new method is more accurate and efficient than the widely used Gaussian Mixture Model(GMM) which uses the same feature vector.

Key words: speaker recognition, micro-clustering, compressed parameter

中图分类号: 

• TN912.3