GA Approaches to HMM Optimization for Automatic Speech Recognition

  • Yara Pérez Maldonado
  • Santiago Omar Caballero Morales
  • Roberto Omar Cruz Ortega
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7329)

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.

Keywords

genetic algorithms hidden markov model automatic speech recognition 

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Co. (1989)Google Scholar
  4. 4.
    Hong, Q.Y., Kwong, S.: A genetic classification method for speaker recognition. Engineering Applications of Artificial Intelligence 18, 13–19 (2005)CrossRefGoogle Scholar
  5. 5.
    Jurafsky, D., Martin, J.H.: Speech and Language Processing. Prentice Hall, Pearson (2009)Google Scholar
  6. 6.
    Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE 37, 257–286 (1989)CrossRefGoogle Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Young, S., Woodland, P.: The HTK Book (for HTK Version 3.4). Cambridge University Engineering Department (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yara Pérez Maldonado
    • 1
  • Santiago Omar Caballero Morales
    • 1
  • Roberto Omar Cruz Ortega
    • 1
  1. 1.Technological University of the Mixteca, UTMHuajuapan de LeonMexico

Personalised recommendations