PSO Algorithm for Speaker Verification
碩士 === 國立中央大學 === 電機工程研究所 === 100 === This thesis proposed method uses PSO algorithm to develop the VQ algorithm and determinate the parameter of SVM. Particle swarm optimization (PSO) simulates social behavior such as birds flocking to a promising position to achieve precise objectives in a multi-d...
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| Format: | Others |
| Language: | zh-TW |
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2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/19867975602889928362 |
| Summary: | 碩士 === 國立中央大學 === 電機工程研究所 === 100 === This thesis proposed method uses PSO algorithm to develop the VQ algorithm and determinate the parameter of SVM. Particle swarm optimization (PSO) simulates social behavior such as birds flocking to a promising position to achieve precise objectives in a multi-dimensional space. PSO performs searches using a population (called swarm) of individuals (called particles) that are updated from iteration to iteration.
The vector quantization (VQ) was a powerful technique in the applications of digital speech compression. The traditionally widely used method such as the Linde-Buzo-Gray (LBG) algorithm always generated local optimal codebook. This thesis proposed method uses PSO algorithm to develop the VQ algorithm. Experimental results showed that the PSO algorithm can provide a better codebook with smaller mean square error (MSE) and less computation time than LBG algorithm.
In the support vector machines (SVM), the model for classification is generated from the training process with the training data. Later on, classification is executed based on trained model. The largest problems encountered in setting up the SVM model are how to select the kernel function and its parameter values. This thesis proposed a method uses PSO algorithm to determinate the SVM parameter. Experimental results showed that the proposed system obtains a 2.26% EER and 0.0275 DCF improvement over the system with grid search.
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