GA approaches to HMM optimization for automatic speech recognition

Authors:
Yara Pérez Maldonado;Santiago Omar Caballero Morales;Roberto Omar Cruz Ortega
Affiliations:
Technological University of the Mixteca, UTM, Huajuapan de Leon, Oaxaca, Mexico;Technological University of the Mixteca, UTM, Huajuapan de Leon, Oaxaca, Mexico;Technological University of the Mixteca, UTM, Huajuapan de Leon, Oaxaca, Mexico
Venue:
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Year:
2012

Citing 3
Cited 0

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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.