论文信息 - Text-independent speaker recognition using support vector machine

Text-independent speaker recognition using support vector machine

The support vector machine (SVM) is an important learning method of statistical learning theory, and is also a powerful tool for pattern recognition problems. This paper studies the speaker identification and verification problem using a support vector machine, and presents a SVM training method on large-scale samples according to the speech signal. A text-independent speaker recognition system based on SVM was implemented and the results show good performance.

Wang Bingxi | Hou Fenglei

[1] Colin Campbell,et al. Algorithmic approaches to training Support Vector Machines: a survey , 2000, ESANN.

[2] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[3] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[4] Federico Girosi,et al. Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .

[6] Michael S. Schmidt,et al. Identifying Speakers With Support Vector Networks , 1996 .