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Speaker Recognition Based on Swarm Intelligence and Blind Source Separation

Author: LiWeiJuan
Tutor: LiMing
School: Lanzhou University of Technology
Course: Communication and Information System
Keywords: Speaker Recognition Independent Component Analysis Particle swarm classifier Support Vector Machine
CLC: TN912.34
Type: Master's thesis
Year: 2010
Downloads: 44
Quote: 0
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Abstract


Speaker recognition in clean speech conditions have obtained good recognition rate , but because of the presence of noise , the voice signal is distorted , resulting in the training environment and test environment does not match , a serious impact on the recognition rate . Reflect the speaker's personality traits extracted from complex background noise speech parameters and design effective classification is the speaker recognition applied to the actual difficulty . The problems above , the departure From Noisy Speech parameter extraction and classification of these two aspects of the design , to put forward their own solutions , and experiments to test its feasibility . The main work includes : 1 for speaker identification in mixed noise environment affect the accuracy of speech recognition , consider the ICA algorithm is improved and applied to the speech signal de-noising process , blind source separation algorithm based on independent component analysis traditional implementations are based on the gradient , the convergence performance and solving performance depends on the choice of learning step , and the convergence is slow . In order to overcome these shortcomings, particle swarm algorithm to improve the independent component analysis , MFCC coefficients to enhance speech features . The test results show that the improved ICA algorithm can quickly and efficiently get the optimal solution of the BSS can effectively suppress ambient noise , thereby improving voice quality . For support vector machine training in the case of a large sample of slow shortcomings with Weight optimal location strategy to improve the quantum particle swarm optimization , through improved Michigan encoding scheme to encode the voice parameters , the tectonic classification rules fitness function , to achieve classifier design based on the weighted quantum particle swarm . Speaker recognition results show that the classifier has better anti-noise performance and higher recognition speed simulation results show that the improved WQPS-classifier and other classifiers , better anti-noise performance and identify speed.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Electro-acoustic technology and speech signal processing > Speech Signal Processing > Speech Recognition and equipment
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