Optimization of Hidden Markov Model With Gaussian Mixture Densities for Arabic Speech Recognition

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Abstract: Speech recognition applications are becoming more and more useful nowadays. In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have been widely used for modeling the temporal speech signal. Iterative algorithms such as Forward - Backward or Baum-Welch are commonly used to locally optimize HMM parameters (i.e., observation and transition probabilities). In this paper we propose a general approach based on Genetic Algorithms (GAs) to evolve HMM with Gaussian mixture densities. The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values. For this purpose, a sample database containing speech files of Algerian speakers is used.

WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 15, 2019, Art. #11

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Abdelmadjid Benmachiche, Amina Makhlouf, "Optimization of Hidden Markov Model With Gaussian Mixture Densities for Arabic Speech Recognition," WSEAS Transactions on Signal Processing, vol. 15, pp. 85-95, 2019, DOI:
Abdelmadjid Benmachiche, Amina Makhlouf. Optimization of Hidden Markov Model With Gaussian Mixture Densities for Arabic Speech Recognition. WSEAS Transactions on Signal Processing. 2019;15:85-95.
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