Improved Text-Independent Speaker Identification using Fused MFCC and IMFCC Feature Sets based on Gaussian Filter
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 speech related applications. On a recent
contribution by authors, it has been shown that the Inverted Mel-
Frequency Cepstral Coefficients (IMFCC) is useful feature set for
SI, which contains complementary information present in high
frequency region. This paper introduces the Gaussian shaped filter
(GF) while calculating MFCC and IMFCC in place of typical
triangular shaped bins. The objective is to introduce a higher
amount of correlation between subband outputs. The performances
of both MFCC & IMFCC improve with GF over conventional
triangular filter (TF) based implementation, individually as well as
in combination. With GMM as speaker modeling paradigm, the
performances of proposed GF based MFCC and IMFCC in
individual and fused mode have been verified in two standard
databases YOHO, (Microphone Speech) and POLYCOST
(Telephone Speech) each of which has more than 130 speakers.
[1] J. P. Cambell, Jr., "Speaker Recognition:A Tutorial", Proceedings of
The IEEE, vol. 85, no. 9, pp. 1437-1462, Sept. 1997.
[2] 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
[3] 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.
[4] 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.
[5] Harrag A. Mohamadi T., Serignat J.F., "LDA Combination of Pitch
and MFCC Features in Speaker Recognition", Proceedings of
INDICON 2005, pp. 237-240, 11-13 Dec., IIT Chennai, India, 2005.
[6] 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.
[7] 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.
[8] Chakroborty, S., Roy, A. and Saha, G., "Improved Closed set Text-
Independent Speaker Identification by Combining MFCC with
Evidence from Flipped Filter Banks". International Journal of Signal
Processing, Vol. 4, No. 2, Page(s):114-122, 2007.
[9] J. Kittler, M. Hatef, R. Duin, J. Mataz, "On combining classifiers",
IEEE Trans. Pattern Anal. Mach. Intell. 20 (1998) 226-239.
[10] 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.
[11] Laurent Besacier and Jean-Francois Bonastre, "Subband architechute
for automatic speaker recognition", Signal Processing, vol-80, pp.
1245-1259, 2000.
[12] R. P. Lippmann, ``Speech recognition by machines and humans",
Speech Communication, vol. 22, No. 1, pp. 1-15, 1997.
[13] 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.
[14] 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.
[15] J. Campbell, "Testing with the YOHO CDROM voice verification
corpus", ICASSP95, 1995, vol.1 pp. 341-344.
[16] Petrovska, D., et al. "POLYCOST: A Telephone-Speech Database for
Speaker Recognition", RLA2C, Avignon, France, April 20-23, 1998,
pp. 211-214.
[17] D. O- Shaughnessy, Speech Communication Human and Machine,
Addison-Wesley, New York, 1987.
[18] Ben Gold and Nelson Morgan, Speech and Audio Signal Processing,
Part- IV, Chap.14, pp. 189-203, John Willy & Sons ,2002.
[19] Daniel J. Mashao, Marshalleno Skosan, "Combining Classifier
Decisions for Robust Speaker Identification", Pattern Recog,, vol. 39,
pp. 147-155, 2006.
[20] A. Papoulis and S. U. Pillai, "Probability, Random variables and
Stochastic Processes", Tata McGraw-Hill Edition, Fourth Edition,
Chap. 4, pp. 72-122, 2002.
[21] Y. Linde, A. Buzo, and R. M. Gray, "An algorithm for vector quantizer
design", IEEE Trans. Commun., vol. 28, no. 1, pp. 84-95, 1980.
[22] Daniel Garcia-Romero, Julian Fierrez-Aguilar, Joaquin Gonzalez-
Rodriguez, Javier Ortega-Garcia, "Using quality measures for
multilevel speaker recognition", Computer Speech and Language, Vol.
20, Issue 2-3, pp. 192-209, Apr. 2006,
[23] 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.
[24] 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.
[1] J. P. Cambell, Jr., "Speaker Recognition:A Tutorial", Proceedings of
The IEEE, vol. 85, no. 9, pp. 1437-1462, Sept. 1997.
[2] 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
[3] 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.
[4] 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.
[5] Harrag A. Mohamadi T., Serignat J.F., "LDA Combination of Pitch
and MFCC Features in Speaker Recognition", Proceedings of
INDICON 2005, pp. 237-240, 11-13 Dec., IIT Chennai, India, 2005.
[6] 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.
[7] 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.
[8] Chakroborty, S., Roy, A. and Saha, G., "Improved Closed set Text-
Independent Speaker Identification by Combining MFCC with
Evidence from Flipped Filter Banks". International Journal of Signal
Processing, Vol. 4, No. 2, Page(s):114-122, 2007.
[9] J. Kittler, M. Hatef, R. Duin, J. Mataz, "On combining classifiers",
IEEE Trans. Pattern Anal. Mach. Intell. 20 (1998) 226-239.
[10] 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.
[11] Laurent Besacier and Jean-Francois Bonastre, "Subband architechute
for automatic speaker recognition", Signal Processing, vol-80, pp.
1245-1259, 2000.
[12] R. P. Lippmann, ``Speech recognition by machines and humans",
Speech Communication, vol. 22, No. 1, pp. 1-15, 1997.
[13] 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.
[14] 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.
[15] J. Campbell, "Testing with the YOHO CDROM voice verification
corpus", ICASSP95, 1995, vol.1 pp. 341-344.
[16] Petrovska, D., et al. "POLYCOST: A Telephone-Speech Database for
Speaker Recognition", RLA2C, Avignon, France, April 20-23, 1998,
pp. 211-214.
[17] D. O- Shaughnessy, Speech Communication Human and Machine,
Addison-Wesley, New York, 1987.
[18] Ben Gold and Nelson Morgan, Speech and Audio Signal Processing,
Part- IV, Chap.14, pp. 189-203, John Willy & Sons ,2002.
[19] Daniel J. Mashao, Marshalleno Skosan, "Combining Classifier
Decisions for Robust Speaker Identification", Pattern Recog,, vol. 39,
pp. 147-155, 2006.
[20] A. Papoulis and S. U. Pillai, "Probability, Random variables and
Stochastic Processes", Tata McGraw-Hill Edition, Fourth Edition,
Chap. 4, pp. 72-122, 2002.
[21] Y. Linde, A. Buzo, and R. M. Gray, "An algorithm for vector quantizer
design", IEEE Trans. Commun., vol. 28, no. 1, pp. 84-95, 1980.
[22] Daniel Garcia-Romero, Julian Fierrez-Aguilar, Joaquin Gonzalez-
Rodriguez, Javier Ortega-Garcia, "Using quality measures for
multilevel speaker recognition", Computer Speech and Language, Vol.
20, Issue 2-3, pp. 192-209, Apr. 2006,
[23] 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.
[24] 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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:58915", author = "Sandipan Chakroborty and Goutam Saha", title = "Improved Text-Independent Speaker Identification using Fused MFCC and IMFCC Feature Sets based on Gaussian Filter", 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 speech related applications. On a recent
contribution by authors, it has been shown that the Inverted Mel-
Frequency Cepstral Coefficients (IMFCC) is useful feature set for
SI, which contains complementary information present in high
frequency region. This paper introduces the Gaussian shaped filter
(GF) while calculating MFCC and IMFCC in place of typical
triangular shaped bins. The objective is to introduce a higher
amount of correlation between subband outputs. The performances
of both MFCC & IMFCC improve with GF over conventional
triangular filter (TF) based implementation, individually as well as
in combination. With GMM as speaker modeling paradigm, the
performances of proposed GF based MFCC and IMFCC in
individual and fused mode have been verified in two standard
databases YOHO, (Microphone Speech) and POLYCOST
(Telephone Speech) each of which has more than 130 speakers.", keywords = "Gaussian Filter, Triangular Filter, Subbands,Correlation, MFCC, IMFCC, GMM.", volume = "3", number = "11", pages = "2105-9", }
