Novel approach in speaker identification using support vector machines

This paper presents a novel approach on speaker identification using support vector machines (SVMs). To improve the performance of the identification, an extra training set is applied to train a discrete density hidden markov model (HMM). In testing session, first, the multi-class-SVM classifies each feature vector. Then, the HMM model is applied to make a decision with the classes sequence. HMM-based technique outperforms the conventional methods, especially when there are not enough training or testing data. While the proposed method doesnpsilat induce much computational complexities, it reduces the identification error rates up to 57.14%.

[1]  M. Savic,et al.  A TMs32020-based real time, text-independent, automatic speaker verification system , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[2]  Naftali Z. Tisby.  On the application of mixture AR hidden Markov models to text independent speaker recognition , 1991, IEEE Trans. Signal Process..

[3]  Douglas A. Reynolds,et al.  Robust text-independent speaker identification using Gaussian mixture speaker models , 1995, IEEE Trans. Speech Audio Process..

[4]  William M. Campbell,et al.  Support vector machines for speaker and language recognition , 2006, Comput. Speech Lang..

[5]  Wei Zhang,et al.  Text-independent speaker recognition by combining speaker-specific GMM with speaker adapted syllable-based HMM , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  Tsuhan Chen,et al.  Integration strategies for audio-visual speech processing: applied to text-dependent speaker recognition , 2005, IEEE Transactions on Multimedia.

[7]  Lars Kai Hansen,et al.  A New Database for Speaker Recognition , 2005 .

[8]  E. H. Wrench,et al.  Text‐independent speaker recognition with short utterances , 1982 .

[9]  Douglas A. Reynolds,et al.  Text-dependent speaker verification using decoupled and integrated speaker and speech recognizers , 1995, EUROSPEECH.

[10]  Steve Renals,et al.  SVMSVM: support vector machine speaker verification methodology , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[11]  Til T. Phan,et al.  Text-Independent Speaker Identification , 1999 .

[12]  Jr. J.P. Campbell,et al.  Speaker recognition: a tutorial , 1997, Proc. IEEE.

[13]  K. P. Li,et al.  An approach to text-independent speaker recognition with short utterances , 1983, ICASSP.

[14]  Douglas A. Reynolds,et al.  An overview of automatic speaker recognition technology , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[15]  Robert Tibshirani,et al.  Classification by Pairwise Coupling , 1997, NIPS.

[16]  William M. Campbell,et al.  A SVM/HMM system for speaker recognition , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[17]  R. Wohlford,et al.  A new method of text-independent speaker recognition , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[18]  Jason Weston,et al.  Multi-Class Support Vector Machines , 1998 .