Study Of Speech Recognition Algorithm Based On HMM And Neural Network

Speech recognition is the research hotspot in the field of speech signal processing. It has been a difficult problem for a long time, especially for the recognition of person-independent and in noisy environment. This paper discussed several common speech recognition methods including classical Hidden Markov Model and artificial neural network which is very popular currently. It also introduced a new anti-noise feature parameter, Zero-crossings with Peak-amplitudes feature (ZCPA feature), which can be used to construct a robust speech recognition system.This paper presented several familiar feature extracting methods such as Linear Prediction Cepstrum Coefficient (LPCC) and Mel Frequency Cepstrum Coefficient (MFCC). They have got excellent recognition results under clean environment, but their performance will deteriorate severely in noisy condition. So most part is devoted to introduce ZCPA feature and analyze its anti-noise principle. Then this paper discussed HMM theory which is used in speech recognition and its implementation process. There areunderflow problems in software implementation procedure for the classical Baum-Welch training algorithm, and a lot of literatures did not presented an explicit method. With respect to this problem, this paper inducted the scaling algorithm and derived the reestimate formulae of Baum-Welch algorithm again. The experiments showed that it can converge rapidly and the recognition results are good, which proved the correctness of the new reestimate formulae, while the old formulae can not converge in experiments. Then the paper studied several feed-forward neural networks used in classification including BP network, RBF network and wavelet network. It discussed their theories, learning process and the modeling method for speech recognition respectively. Centroid selecting of RBF hidden nodes has great influence for the network performance. The common K-means clustering is a kind of unsupervised learning method, the paper proposed to cluster the input data by the classification information of training samples and calculate their centroids to be the centers of each hidden function. Experiment results showed that the recognition rate by selecting the centroids of hidden functions supervised is better than K-means clustering method. Finally, the paper introduced wavelet transform theory, and the Gaussian basis function of RBF network was taken place by a wavelet basis function, so a wavelet neural network can be formed. Experiments showed that the wavelet network also can get excellent recognition performance.