@inproceedings{25aed2b6faee46c1a995f95d5cb3a902,
title = "Optimization of HHM by a genetic algorithm",
abstract = "Hidden Markov Model (HMM) is a natural and highly robust statistical methodology for automatic speech recognition. It is also being tested and proved considerably in a wide range of applications. The model parameters of the HMM are essence in describing the behavior of the utterance of the speech segments. Many successful heuristic algorithms are developed to optimize the model parameters in order to best describe the trained observation sequences. However, all these methodologies are exploring for only one local maxima in practice. No one methodology can recovering from the local maxima to obtain the global maxima or other more optimized local maxima. In this paper, a stochastic search method called Genetic Algorithm (GA) is presented for HM training. GA mimics natural evolution and perform global searching within the defined searching space. Experimental results showed that using GA for HMM training (GA-HMM training) has a better performance than using other heuristic algorithms.",
author = "CHAU, {C. W.} and S. KWONG and DIU, {C. K.} and FAHRNER, {W. R.}",
year = "1997",
doi = "10.1109/ICASSP.1997.598857",
language = "English",
isbn = "0818679190",
volume = "3",
series = "Proceedings : ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",
publisher = "IEEE",
pages = "1727--1730",
booktitle = "Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing",
note = "1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ; Conference date: 21-04-1997 Through 24-04-1997",
}