Abstract
Automatic speech recognition (ASR) systems follow a well established approach of pattern recognition, that is signal processing based feature extraction at front-end and likelihood evaluation of feature vectors at back-end. Mel-frequency cepstral coefficients (MFCCs) are the features widely used in state-of-the-art ASR systems, which are derived by logarithmic spectral energies of the speech signal using Mel-scale filterbank. In filterbank analysis of MFCC there is no consensus for the spacing and number of filters used in various noise conditions and applications. In this paper, we propose a novel approach to use particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the parameters of MFCC filterbank such as the central and side frequencies. The experimental results show that the new front-end outperforms the conventional MFCC technique. All the investigations are conducted using two separate classifiers, HMM and MLP, for Hindi vowels recognition in typical field condition as well as in noisy environment.
Similar content being viewed by others
References
Aggarwal, R. K., & Dave, M. (2011a). Performance evaluation of sequentially combined heterogeneous feature streams for Hindi speech recognition system. Telecommunication Systems Journal. doi:10.1007/s11235-011-9623-0. Special issue on signal processing applications in human computer interaction.
Aggarwal, R. K., & Dave, M. (2011b). Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I). International Journal of Speech Technology, 14(4), 297–308.
Aggarwal, R. K., & Dave, M. (2011c). Acoustic modeling problem for automatic speech recognition system: advances and refinements (Part II). International Journal of Speech Technology, 14(4), 309–320.
Benesty, J., Sondhi, M.M., & Huang, Y. (2008). Handbook of speech processing. Berlin: Springer.
Boll, S. F. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech, and Signal Processing, 27, 113–120.
Burget, L., & Hermansky, H. (2001). Data driven design of filterbank for speech recognition. In Lecture notes in computer science: Vol. 2166. Text, speech and dialogue (pp. 299–304). Berlin: Springer.
Chau, C. W., Kwong, S., Diu, C. K., & Fahrner, W. R. (1997). Optimization of HMM by a genetic algorithm. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (pp. 1727–1730).
Chen, J., Benesty, J., Huang, Y., & Doclo, S. (2006). New insights into the noise reduction Wiener filter. IEEE Transactions on Audio, Speech, & Language Processing, 14(4), 1218–1234.
Davis, S., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28, 357–366.
Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–56.
Gales, M., & Young, S. (1996). Robust continuous speech recognition using parallel model combination. IEEE Transactions on Speech and Audio Processing, 4(5), 352–359.
Hermansky, H. (1990). Perceptually predictive (PLP) analysis of speech. The Journal of the Acoustical Society of America, 87, 1738–1752.
Hermansky, H., & Morgan, N. (1994). RASTA processing of speech. IEEE Transactions on Speech and Audio Processing, 2(4), 578–589.
Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of international conference on neural networks (pp. 1942–1948). Piscataway: IEEE.
Kennedy, J., Eberhart, R.C., & Shi, Y. (2001). Swarm intelligence. San Mateo: Morgan Kaufmann.
Koehler, J., Morgan, N., Hermansky, H., Hirsch, H. G., & Tong, G. (1994). Integrating RASTA-PLP into speech recognition. In Proceedings IEEE international conference on acoustics, speech and signal processing (Vol. 1, pp. 421–424).
Kwong, S., Chau, C. W., & Halang, W. A. (1996). Genetic algorithm for optimizing the nonlinear time alignment of automatic speech recognition systems. IEEE Transactions on Industrial Electronics, 43(5), 559–566.
Kwong, S., Chau, C. W., Man, K. F., & Tang, K. S. (2001). Optimization of HMM topology and its model parameters by genetic algorithms. Pattern Recognition, 34(2), 509–522.
Kwong, S., He, Q. H., Ku, K. W., Chan, T. M., Man, K. F., & Tang, K. S. (2002). A genetic classification error method for speech recognition. Signal Processing, 82, 737–748.
Loizou, P. C., & Spanias, A. S. (1996). High-performance alphabet recognition. IEEE Transactions on Speech and Audio Processing, 4(6), 430–445.
Najkar, N., Razzazi, F., & Sameti, H. (2010). A novel approach to HMM-based speech recognition systems using particle swarm optimization. Mathematical and Computer Modelling, 52, 1910–1920.
Paliwal, K. K. (1987). A speech enhancement method based on Kalman filtering. In Proceedings IEEE ICASSP (pp. 177–180).
Rabanal, P., Rodriguez, I., & Rubio, F. (2009). Applying river formation dynamics to solve NP-complete problems. In Studies in computational intelligence: Vol. 193. Nature-inspired algorithms for optimization (pp. 333–368). Springer, Berlin.
Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286.
Rao, K. S., & Yegnanarayana, B. (2007). Modeling durations of syllables using neural networks. Computer Speech and Language, 21, 282–295.
Rao, K. S. (2011). Role of neural network models for developing speech systems. Sadhana, 36(5), 783–836.
Shi, Y., & Eberhart, R. C. (1998). Parameter selection in particle swarm optimization. In Proceedings of seventh annual conference on evolutionary programming (pp. 591–601).
Skowronski, M. D., & Harris, J. G. (2003). Improving the filterbank of a classic speech feature extraction algorithm. In Proceedings of the IEEE international symposium on circuits and systems (ISCAS’03), (Vol. 4, pp. 281–284).
Skowronski, M. D., & Harris, J. G. (2004). Exploiting independent filter bandwidth of human factor cepstral coefficients in automatic speech recognition. The Journal of the Acoustical Society of America, 116(3), 1774–1780.
Valle, Y. D., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J.-C., & Harley, R. G. (2008). Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 12(2), 171–195.
Varga, A., & Steeneken, H. J. M. (1993). Assessment for automatic recognition: II. NOISEX-92: a database and an experiment to study the effect of additive noise on speech recognition systems. ESCA Journal of Speech Communication, 12(3), 247–251.
Welch, L. R. (2003). HMMs and the Baum-Welch algorithms. IEEE Information Theory Society Newsletter, 53(4), 10–13.
Zheng, F., Zhang, G., & Song, Z. (2001). Comparison of different implementations of MFCC. Journal of Computer Science and Technology, 16(6), 582–589.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Aggarwal, R.K., Dave, M. Filterbank optimization for robust ASR using GA and PSO. Int J Speech Technol 15, 191–201 (2012). https://doi.org/10.1007/s10772-012-9133-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10772-012-9133-9