Support Vector Machine Training for Improved Hidden Markov Modeling
IEEE Transactions on Signal Processing
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Accuracy analysis for wavelet approximations
IEEE Transactions on Neural Networks
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To solve the problem that the performance of speech recognition systems declines in the noisy environment, this paper used the linear predictive Mel frequency cepstrum coefficients according with human hearings characteristic as speech feature parameters, adopted two recognition machines, the support vector machine and the wavelet neural network, realized respectively a Speech recognition system of non-specific person and isolated words with visual C++ programming, got the recognition correct rates in different SNRs and in different words, and compared their recognition results with those of based on traditional hidden Markov models. Experiments indicate that the recognition correct rates based on the support vector machine and the wavelet neural network are all higher than based on traditional hidden Markov models, and also have better robustness.