A Chain of Gaussian Mixture Model for Text-independent Speaker Recognition1
Text-independent speaker recognition has better flexibility than text-dependent method. However, due to the phonetic content difference, the text-independent methods usually achieve lower performance than textdependent method. In order to combining the flexibility of text-independent method and the high performance of text-dependent method, we propose a new modeling technique named a chain of Gaussian Mixture Model which encoding the temporal correlation of the training utterance in the chain structure. A special decoding network is then used to evaluate the test utterance to find the best possible phonetic matched segments between test utterance and training utterance. The experimental results indicate that the proposed method significantly improve the system performance, especially for the short test utterance.
Yanxiang Chen Ming Liu
College of Computer Science & Information,Hefei University of Technology, Hefei, Anhui 230009, China Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana,
国际会议
北京
英文
100-103
2009-08-10（万方平台首次上网日期，不代表论文的发表时间）