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Improved Closed Set Text-Independent Speaker Identification by Combining MFCC with Evidence from Flipped Filter Banks

Authors: Sandipan Chakroborty, Anindya Roy, Goutam Saha

Abstract:

A state of the art Speaker Identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for SI applications. However, due to the structure of its filter bank, it captures vocal tract characteristics more effectively in the lower frequency regions. This paper proposes a new set of features using a complementary filter bank structure which improves distinguishability of speaker specific cues present in the higher frequency zone. Unlike high level features that are difficult to extract, the proposed feature set involves little computational burden during the extraction process. When combined with MFCC via a parallel implementation of speaker models, the proposed feature set outperforms baseline MFCC significantly. This proposition is validated by experiments conducted on two different kinds of public databases namely YOHO (microphone speech) and POLYCOST (telephone speech) with Gaussian Mixture Models (GMM) as a Classifier for various model orders.

Keywords: Complementary Information, Filter Bank, GMM, IMFCC, MFCC, Speaker Identification, Speaker Recognition.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330573

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References:


[1] J. P. Cambell, Jr., "Speaker Recognition:A Tutorial", Proceedings of The IEEE, vol. 85, no. 9, pp. 1437-1462, Sept. 1997.
[2] S. B. Davis and P. Mermelstein, "Comparison of Parametric Representation for Monosyllabic Word Recognition in Continuously Spoken Sentences", IEEE Trans. On ASSP, vol. ASSP 28, no. 4, pp. 357-365, Aug. 1980.
[3] R. Vergin, B. O- Shaughnessy and A. Farhat, "Generalized Mel frequency cepstral coefficients for large-vocabulary speakeridenpendent continuous-speech recognition, IEEE Trans. On ASSP, vol. 7, no. 5, pp. 525-532, Sept. 1999.
[4] Ben Gold and Nelson Morgan, Speech and Audio Signal Processing, Part- IV, Chap.14, pp. 189-203, John Willy & Sons,2002.
[5] U. G. Goldstein, "Speaker identifying features based on formant tracks", J. Acoust. Soc. Am, vol. 59, No. 1, pp. 176-182, Jan. 1976.
[6] Rabiner. L, Juang B. H, Fundamentals of speech recognition, Chap. 2, pp. 11-65, Pearson Education, First Indian Reprint, 2003.
[7] Daniel J. Mashao, Marshalleno Skosan, "Combining Classifier Decisions for Robust Speaker Identification", Pattern Recog,, vol. 39, pp. 147-155, 2006.
[8] Zheng F., Zhang, G. and Song, Z., "Comparison of different implementations of MFCC", J. Computer Science & Technology, vol. 16 no. 6, pp. 582-589, Sept. 2001.
[9] Ganchev, T., Fakotakis, N., and Kokkinakis, G. "Comparative Evaluation of Various MFCC Implementations on the Speaker Verification Task", Proc. of SPECOM 2005, October 17-19, 2005. Patras, Greece, vol. 1, pp.191-194.
[10] Faundez-Zanuy M. and Monte-Moreno E., "State-of-the-art in speaker recognition", Aerospace and Electronic Systems Magazine, IEEE, vol. 20, No. 5, pp. 7-12, Mar. 2005.
[11] Yegnanarayana B., Prasanna S.R.M., Zachariah J.M. and Gupta C. S., "Combining evidence from source, suprasegmental and spectral features for a fixed-text speaker verification system", IEEE Trans. Speech and Audio Processing, Vol. 13, No. 4, pp. 575-582, July 2005.
[12] K. Sri Rama Murty and B. Yegnanarayana, "Combining evidence from residual phase and MFCC features for speaker recognition", IEEE Signal Processing Letters, vol 13, no. 1, pp. 52-55, Jan. 2006.
[13] S.R. Mahadeva Prasanna, Cheedella S. Gupta b, B. Yegnanarayana, "Extraction of speaker-specific excitation information from linear prediction residual of speech", Speech Communication, Vol. 48, Issue 10, pp. 1243-1261, October 2006.
[14] D. Reynolds, R. Rose, "Robust text-independent speaker identification using gaussian mixture speaker models", IEEE Trans. Speech Audio Process., vol. 3, no.1, pp. 72-83, Jan. 1995.
[15] D. O- Shaughnessy, Speech Communication Human and Machine,Addison-Wesley, New York, 1987.
[16] J. Kittler, M. Hatef, R. Duin, J. Mataz, "On combining classifiers", IEEE Trans. Pattern Anal. Mach. Intell. 20 (1998) 226-239.
[17] Daniel Garcia-Romero, Julian Fierrez-Aguilar, Joaquin Gonzalez- Rodriguez, Javier Ortega-Garcia, "Using quality measures for multilevel peaker recognition", Computer Speech and Language, Vol. 20, Issue 2- 3, pp. 192-209, Apr. 2006.
[18] J. Campbell, "Testing with the YOHO CDROM voice verification corpus", ICASSP95, 1995, vol.1 pp. 341-344.
[19] Petrovska, D., et al. "POLYCOST: A Telephone-Speech Database for Speaker Recognition", RLA2C, Avignon, France, April 20-23, 1998, pp. 211-214.
[20] H. Melin and J. Lindberg. "Guidelines for experiments on the polycost database", In Proceedings of a COST 250 workshop on Application of Speaker Recognition Techniques in Telephony, pp. 59- 69, Vigo, Spain, November 1996.