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The standard support vector machine (SVM) is a common method of machine learning, the parameters selection of SVM affects the machine learning ability directly. At present, the research on the choice of SVM parameters is still no uniform approach. In order to avoid the difficult problem of selecting parameters, this paper used a deformed SVM, that is, v-SVM, selected parameters of v-SVM by particle...
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...
An important factor that influences the performance of support vector machine is how to select the parameters. Particle swarm optimization is an efficient algorithm and it is broadly used in many research areas like pattern recognition. In order to improve the learning and generalization ability of support vector machine and enhance the speech recognition system accuracy, a method for searching the...
In the noisy environment, the performance of speech recognition system may become worse to some extent. In order to solve this problem, this paper used the zero-crossings with peak amplitudes (ZCPA) features as speech feature parameters, which are based on human hearings property. The extraction method of ZCPA features is that calculating the unward zero-crossing rate of speech signal gets frequency...
Kernel parameters selection of support vector machine is a very important problem, which has great influence on the performance of support vector machine. In order to improve the learning and generalization ability of support vector machine and enhance speech recognition system accuracy, a method of searching for the Gaussian kernel support vector machine optimal parameters(C, ?? ) based on particle...
To improve the generalization ability of the machine learning and solve the problem that recognition rates of the speech recognition system become worse in the noisy environment, a modified Gaussian kernel function which may pay attention to the similar degree between sample space and feature space is proposed. In this paper, used the modified Gaussian kernel support vector machine to a speech recognition...
To improve the learning and generalization ability of the machine-learning model, a new compound kernel that may pay attention to the similar degree between sample space and feature space is proposed. In this paper, used the new compound kernel support vector machine to a speech recognition system for Chinese isolated words, non-specific person and middle glossary quantity, and compared the speech...
Bark wavelet is a new wavelet which is especially designed for speech signal. Its base function satisfies time and bandwidth product least. Moreover, the Bark wavelet divides frequency band based on auditory model. This paper uses Bark wavelet in MFCC. It was used to make preprocessing before FFT. On the other hand, it was used to instead of DCT in MFCC for overcoming the DCT's disadvantage of fixed...
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