The recognition of ten Thai isolated numerals from zero to nine and 60 Thai polysyllabic words are compared between different recognition techniques, namely, Neural Network, Modified Backpropagation Neural Network, Fuzzy-Neural Network, and Hidden Markov Model. The 15-state left-to-right discrete hidden markov model in cooperation with the vector quantization technique has been studied and compared with the multilayer perceptron neural network using the error backpropagation, the modified backpropagation, and also with the fuzzy-neural network with the same configuration. The recognition error on Thai isolated numerals using the conventional neural network, the modified neural network, the fuzzy-neural network, and the hidden markov model techniques are 26.97 percent, 22.00 percent, 8.50 percent, and 15.75 percent respectively.
Cite as: Ahkuputra, V., Jitapunkul, S., Jittiwarangkul, N., Maneenoi, E., Kasuriya, S. (1998) A comparison of Thai speech recognition systems using hidden Markov model, neural network, and fuzzy-neural network. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0283, doi: 10.21437/ICSLP.1998-397
@inproceedings{ahkuputra98_icslp,
author={Visarut Ahkuputra and Somchai Jitapunkul and Nutthacha Jittiwarangkul and Ekkarit Maneenoi and Sawit Kasuriya},
title={{A comparison of Thai speech recognition systems using hidden Markov model, neural network, and fuzzy-neural network}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0283},
doi={10.21437/ICSLP.1998-397}
}