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The research on noisy Tibetan speech recognition algorithm based on wavelet neural network (WNN) combined with auditory feature was carried out in this paper. The recognition classifier based on WNN was designed, and Mel Frequency Cepstrum Constant (MFCC) feature was given. Then the simulation on the given algorithm was run under the different signal to noise ratios (SNR), and the results illustrated...
Adaptive boosting (AdaBoost) learning method can improve the performance of a base classifier by mining feature information in depth. But it is computationally expensive, and the base classifier without a suitable accuracy will cause over fitting. In this paper an improved Adaboost algorithm using maximum a posteriori vector quantization model (VQMAP) for speaker identification is presented. A suitable...
This paper proposes a novel robust speech recognition technique using improved vector Taylor series (VTS) algorithm for embedded systems. It uses a hidden Markov model (HMM) to replace the Gaussian mixture model (GMM) for estimating the clean speech feature, and gives the closed-form solutions of the noise parameters including the mean and variance at each expectation-maximization (EM) iteration....
This paper presents a new model adaptation algorithm using piecewise linear transformation (PLT) for robust speech recognition. In this algorithm, the nonlinear relationship between training and testing mean vectors is approximated by a set of piecewise linear transformations. The PLT coefficients are estimated from adaptation data by the expectation-maximization (EM) algorithm and maximum likelihood...
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