GA Approaches to HMM Optimization for Automatic Speech Recognition

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


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.


genetic algorithms hidden markov model automatic speech recognition 


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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

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