Support vector machines for speaker verification and identification

The performance of the support vector machine (SVM) on a speaker verification task is assessed. Since speaker verification requires binary decisions, support vector machines seem to be a promising candidate to perform the task. A new technique for normalising the polynomial kernel is developed and used to achieve performance comparable to other classifiers on the YOHO database. We also present results on a speaker identification task.

[1]  Herbert Gish,et al.  Speaker identification via support vector classifiers , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[2]  Federico Girosi,et al.  Support Vector Machines: Training and Applications , 1997 .

[3]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[4]  Joseph P. Campbell,et al.  Testing with the YOHO CD-ROM voice verification corpus , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

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

[6]  R. Courant,et al.  Methods of Mathematical Physics , 1962 .

[7]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[8]  Richard J. Mammone,et al.  Speaker recognition using neural networks and conventional classifiers , 1994, IEEE Trans. Speech Audio Process..

[9]  Douglas A. Reynolds,et al.  Speaker identification and verification using Gaussian mixture speaker models , 1995, Speech Commun..

[10]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[11]  William M. Campbell,et al.  Polynomial classifier techniques for speaker verification , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).