GA approaches to HMM optimization for automatic speech recognition

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. © 2012 Springer-Verlag Berlin Heidelberg.