A novel windowing technique for efficient computation of MFCC for speaker recognition

IEEE Signal Processing Letters

Volumn 20, Issue 2, 2013, Pages 149-152

  Sahidullah, Md ^a   Saha, Goutam ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Differentiation in frequency; mel frequency cepstral coefficients (MFCC); power spectrum estimation; speaker recognition; tapered window

Indexed keywords

AUTOMATIC SPEAKER RECOGNITION; CONSISTENT PERFORMANCE; DISCRETE TIME FOURIER TRANSFORM; EFFICIENT COMPUTATION; FREQUENCY DOMAINS; FUNDAMENTAL PROPERTIES; HAMMING WINDOW; MEL-FREQUENCY CEPSTRAL COEFFICIENTS; PHASE INFORMATION; POWER SPECTRUM ESTIMATION; SPEAKER RECOGNITION; SPEAKER RECOGNITION SYSTEM; WINDOW FUNCTIONS; WINDOWING TECHNIQUES;

POWER SPECTRUM; SPECTRUM ANALYSIS;

SPEECH RECOGNITION;

EID: 84872166152     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2012.2235067     Document Type: Article

References (14)

1. F. Harris, "On the use of windows for harmonic analysis with the discrete Fourier transform," Proc. IEEE, vol. 66, no. 1, pp. 51-83, Jan. 1978. (Pubitemid 8338069)
2. J. O. Smith, Spectral Audio Signal Processing. Burlingame, CA: W3K, 2011.
3. M. Mottaghi-Kashtiban and M. Shayesteh, "New efficient window function, replacement for the hamming window," Signal Process., vol. 5, no. 5, pp. 499-505, Aug. 2011.
4. Y. Wang, "An effective approach to finding differentiator window functions based on sinc sum function," Circuits, Syst. Signal Process., vol. 31, no. 5, pp. 1809-1828, Oct. 2012.
5. J. Sandberg, M. Hansson-Sandsten, T. Kinnunen, R. Saeidi, P. Flandrin, and P. Borgnat, "Multitaper estimation of frequency-warped cepstra with application to speaker verification," IEEE Signal Process. Lett., vol. 17, no. 4, pp. 343-346, Apr. 2010.
6. A. Oppenheim, S. Willsky, and S. Nawab, Signals and Systems, 2nd ed. New Delhi, India: PHI Learning, 2009.
7. M. Sahidullah and G. Saha, "Design, analysis and experimental evaluation of block based transformation in mfcc computation for speaker recognition," Speech Commun., vol. 54, no. 4, pp. 543-565, May 2012.
8. S. Nakagawa, L. Wang, and S. Ohtsuka, "Speaker identification and verification by combining mfcc and phase information," IEEE Trans. Audio, Speech, Lang. Process., vol. 20, no. 4, pp. 1085-1095, May 2012.
9. R. M. Hegde, H. A. Murthy, and V. R. R. Gadde, "Significance of the modified group delay feature in speech recognition," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 1, pp. 190-202, Jan. 2007.
10. J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, And Applications, 4th ed. Upper Saddle River, NJ: Pearson, 2007.
11. D. Reynolds, T. Quatieri, and D. R. B., "Speaker verification using adapted Gaussian mixture models," Digital Signal Process., vol. 10, no. 1-3, pp. 19-41, 2000. (Pubitemid 30592166)
12. W. Campbell, D. Sturim, and D. Reynolds, "Support vector machines using gmm supervectors for speaker verification," IEEE Signal Process. Lett., vol. 13, no. 5, pp. 308-311, May 2006.
13. W. Campbell,D. Sturim,D. Reynolds, andA. Solomonoff, "Svm based speaker verification using a gmmsupervector kernel and nap variability compensation," in IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP 2006), May 2006, vol. 1, p. I.
14. T. Kinnunen, R. Saeidi, F. Sedlak, K. A. Lee, J. Sandberg,M. Hansson-Sandsten, and H. Li, "Low-variance multitaper mfcc features: A case study in robust speaker verification," IEEE Trans. Audio, Speech, Lang. Process., vol. 20, no. 7, pp. 1990-2001, Sept. 2012.