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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...
The problem of detection for small object descript by image would be investigated, and the faster detection algorithm based on the genetic algorithm and partial fractal dimension would be presented in this paper. The stability of the fractal dimension used in the small object detection and the real-time performance provided by multi-dot searching algorithm provided by genetic algorithm would be fully...
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