Spectral Analysis of Speech: A New Technique

Authors: Neeta Awasthy, J.P.Saini, D.S.Chauhan

Abstract:

ICA which is generally used for blind source separation problem has been tested for feature extraction in Speech recognition system to replace the phoneme based approach of MFCC. Applying the Cepstral coefficients generated to ICA as preprocessing has developed a new signal processing approach. This gives much better results against MFCC and ICA separately, both for word and speaker recognition. The mixing matrix A is different before and after MFCC as expected. As Mel is a nonlinear scale. However, cepstrals generated from Linear Predictive Coefficient being independent prove to be the right candidate for ICA. Matlab is the tool used for all comparisons. The database used is samples of ISOLET.

Keywords: Cepstral Coefficient, Distance measures, Independent Component Analysis, Linear Predictive Coefficients.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058183

References:

[1] Davis S. and P.Mermelstein," Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences," IEEE Trans. ASSP 28,pp.357-366,1980.
[2] Joseph W. Picone, "Signal Modeling Techniques in speech recognition," Proceedings of the IEEE, vol.81, no.9, pp.1215- 1247,1993.
[3] Jutten C. and Herault, "Blind Separation of Sources, Part I: An adaptive algorithm based on a neuromimetic architecture," Signal Process., vol.24, no.1, pp.1-10,1991.
[4] Hyvarinen A., "A family of fixed-point algorithms for Independent Component Analysis," ICASSP, pp 3917-3920, 1997.
[5] Blaschke and Laurenz Wiskott, "CuBICA: Independent component analysis by simultaneous third and fourth order cumulant diagonalization," IEEE Trans. on Signal Processing, vol.52, no.3, pp.1250-1256,2004.
[6] Hyvarinen A. and Erkki Oja, "Independent Component Analysis: Algorithms and Applications", http://www.cis.hut.fi/projects/ica/
[7] Pierre Comon, "Independent Component Analysis, A new concept?," Signal Processing, 36, pp.287-314,1994.
[8] Lawrence Rabiner & Biing-Hwang Juang, Fundamentals of Speech Recognition. Pearson Education, 2003.
[9] The software generated for this purpose may be referred by sending a mail at [email protected].
[10] Kishore S. Trivedi, ÔÇÿProbability and Statistics with Reliability, Queing & Computer Science Applications-, PHI, 1999.