Speaker Authentication Using Vector Quantization
Main Article Content
Abstract
In this paper, the role of the vector quantization in the speaker authentication system was studied. Vector quantization based speaker authentication system was considered in two phases; training and testing. The training phase concerned with enrolling the speaker models to build the codebook. The codebook generated from a set of feature vectors belong to each sample of speaker's voice. The testing phase includes matching the unknown input speaker with the models. The matching is performed by evaluating the similarity measure between the unknown speech sample and the models in the speaker database to authenticate the input speaker. A weighted similarity measure was introduced; it takes into regard the correlations between the known models in the database. Larger weights are assigned to vectors that have high discriminating power between the speakers and vice versa. The proposed system gave an encourage results; the authentication rate was about 86.6% during a time 4 s.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
- Furui S., 1997, Recent advances in speaker recognition. Pattern Recognition Letters, 18: 859-872,
- Rubust Q,,J., 2007, Speaker Recognition,Thesis Submitted to School of Computer Science,Carenegie Mellon University
- Atal B. , 1974 Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification. Journal of the Acoustic Society of America, 55(6):1304–1312
- Furui, S. , 1981 ”Cepstral analysis technique for automatic speaker verification,” IEEE Transactions on Acoustics, Speech and Signal Processing, 29(2): 254-272
- Campbell J. , 1997 Speaker recognition: a tutorial. Proceedings of the IEEE, 85(9):1437–1462
- Besacier L. and Bonastre J. , 2000 Subband architecture for automatic speaker recognition. Signal Processing, 80:1245–1259
- Besacier L. , Bonastre J., and Fredouille C., 2000, Localization and selection of speaker-specific information with statistical modeling. Speech Communications, 31:89–106.
- Kinnunen T., 2002, Designing a speaker-discriminative filter bank for speaker recognition. In Proc. Int. Conf. on Spoken Language Processing (ICSLP 2002), pages 2325–2328, Denver, Colorado, USA.
- Sivakumaran P., Ariyaeeinia A., and Loomes M., 2003 Sub-band based text-dependent speaker verification. Speech Communications, 41:485–509.
- Damper R. and Higgins J., 2003, Improving speaker identification in noise by subband processing and decision fusion. Pattern Recognition Letters, 24:2167–2173.
- Ming J., Stewart D., Hanna P., Corr P., Smith J., and Vaseghi S.,2003, Robust speaker identification using posterior union models. In Proc. 8th European Conference on Speech Communication and Technology (Eurospeech 2003), pages 2645–2648, Geneva, Switzerland.
- Soong F.K., Rosenberg A.E., Juang B.-H., and Rabiner L.R., 1987. A vector quantization approach to speaker recognition. AT & T Technical Journal, 66:14–26.
- Kinnunen T., Kilpel¨ainen T., and Fr¨anti P., 2000 Comparison of clustering algorithms in speaker identification. In Proc. IASTED Int. Conf. Signal Processing and Communications (SPC 2000), pages 222–227, Marbella, Spain.
- Kinnunen T. and Fr¨anti P. , 2001 Speaker discriminative weighting method for VQ-based speaker identification. In Proc. Audio- and Video-Based Biometric Authentication (AVBPA 2001), pages 150–156, Halmstad, Sweden.
- Fan N. and Rosca J. , 2003 Enhanced VQ-based algorithms for speech independent speaker identification.In Proc. Audio- and Video-Based Biometric Authentication (AVBPA 2003), pages 470–477, Guildford, UK.
- Reynolds D.A. and Rose R.C.. 1995. Robust text-independent speaker identification using gaussian mixture speaker models. IEEE Trans. on Speech and Audio Processing,3: 72–83.
- Reynolds D.A., Quatieri T.F., and Dunn R.B. , 2000. Speaker verification using adapted gaussian mixture models.Digital Signal Processing, 10(1):19–41.
- Bimbot F. and Mathan L. , 1993 Text-free speaker recognition using an arithmetic-harmonic sphericity mesure. In Proc. 3th European Conference on Speech Communication and Technology (Eurospeech 1993), pages 169–172, Berlin, Germany
- Farrell K.R., Mammone R.J., and Assaleh K. T., 1994.Speaker recognition using neural networks and conventional classifiers. IEEE Trans. on Speech and Audio Processing, 2(1):194–205.
- He, J., Liu, L., and Palm, G. , 1999 ”A discriminative training algorithm for VQ-based speaker identification,” IEEE Transactions on Speech and Audio Processing, 7(3): 353-356.
- Jin, Q., 2000, A.: “A naive de-lambing method for speaker identification,” Proc. ICSLP 2002, Beijing, China.
- Kinnunen T., Kilpeläinen T., Fränti P., 2000:”Comparison of clustering algorithms in speaker identification,” Proc. IASTED Int. Conf. Signal Processing and Communications (SPC): 222-227, Marbella, Spain
- Soong F.K., Rosenberg A.E., Juang B-H., and Rabiner, L.R., 1987 ”A vector quantization approach to speaker recognition,” AT&T Technical Journal, 66: 14-26,.