Abstract
Hidden Markov Models (HMMs) have been widely used for Automatic Speech Recognition (ASR). Iterative algorithms such as Forward - Backward or Baum-Welch are commonly used to locally optimize HMM parameters (i.e., observation and transition probabilities). However, finding more suitable transition probabilities for the HMMs, which may be phoneme-dependent, may be achievable with other techniques. In this paper we study the application of two Genetic Algorithms (GA) to accomplish this task, obtaining statistically significant improvements on un-adapted and adapted Speaker Independent HMMs when tested with different users.
Chapter PDF
Similar content being viewed by others
References
Chan, C.W., Kwong, S., Man, K.F., Tang, K.S.: Optimization of hmm topology and its model parameters by genetic algorithms. Pattern Recognition 34, 509–522 (2001)
Gillick, L., Cox, S.J.: Some statistical issues in the comparison of speech recognition algorithms. In: Proc. IEEE Conf. on Acoustics, Speech and Signal Processing, pp. 532–535 (1989)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Co. (1989)
Hong, Q.Y., Kwong, S.: A genetic classification method for speaker recognition. Engineering Applications of Artificial Intelligence 18, 13–19 (2005)
Jurafsky, D., Martin, J.H.: Speech and Language Processing. Prentice Hall, Pearson (2009)
Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 37, 257–286 (1989)
Robinson, T.: WSJCAM0: A british english speech corpus for large vocabulary continuous speech recognition. In: Proc. IEEE Conf. on Acoustics, Speech and Signal Processing, pp. 81–84 (1995)
Takara, T., Iha, Y., Nagayama, I.: Selection of the optimal structure of the continuous hmm using the genetic algorithm. In: Proceedings of ICSLP 1998 (1998)
Xiao, J., Zou, L., Li, C.: Optimization of hidden markov model by a genetic algorithm for web information extraction. In: Proc. of the International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2007 (2007)
Young, S., Woodland, P.: The HTK Book (for HTK Version 3.4). Cambridge University Engineering Department (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pérez Maldonado, Y., Caballero Morales, S.O., Cruz Ortega, R.O. (2012). GA Approaches to HMM Optimization for Automatic Speech Recognition. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera López, J.A., Boyer, K.L. (eds) Pattern Recognition. MCPR 2012. Lecture Notes in Computer Science, vol 7329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31149-9_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-31149-9_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31148-2
Online ISBN: 978-3-642-31149-9
eBook Packages: Computer ScienceComputer Science (R0)
