Discriminative transformation for speech features based on genetic algorithm and HMM likelihoods

Behzad Zamani, Ahmad Akbari, Babak Nasersharif, Mehdi Mohammadi, Azarakhsh Jalalvand

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Hidden Markov Model (HMM) is a well-known classification approach which its parameters are conventionally learned using maximum likelihood (ML) criterion based on expectation maximization algorithm. Improving of parameter learning beyond ML has been performed based on the concept of discrimination among classes in contrast to maximizing likelihood of each individual class. In this paper, we propose a discriminative feature transformation method based on genetic algorithm, to increase Hidden Markov Model likelihoods in its training phase for a better class discrimination. The method is evaluated for phoneme recognition on clean and noisy TIMIT. Experimental results demonstrate that the proposed transformation method results in higher phone recognition rate than well-known feature transformations methods and conventional HMM learning algorithm based on ML criterion.

Original languageEnglish (US)
Pages (from-to)247-253
Number of pages7
JournalIEICE Electronics Express
Issue number4
StatePublished - Feb 25 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Electrical and Electronic Engineering


  • Genetic algorithm
  • Minimum classification error
  • Speech recognition


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