Automatic Speech Recognition Using Support Vector Machine and Particle Swarm Optimization

被引：0

作者：

Batista, Gracieth Cavalcanti ^{[1
]}

Santos Silva, Washington Luis ^{[2
]}

Menezes, Angelo Garangau ^{[3
]}

机构：

[1] Fed Inst Maranhao, Elect Engn, Sao Luis, Maranhao, Brazil

[2] Fed Inst Maranhao, Dept Elect & Elect, Sao Luis, Maranhao, Brazil

[3] Univ Tiradentes, Mechatron Engn, Aracaju, SE, Brazil

来源：

PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) | 2016年

关键词：

Support Vector Machines; Particle Swarm Optimization; Pattern Recognition; Statistical Learning Theory; Automatic Speech Recognition;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Support Vector Machine (SVM) is an algorithm that trains and classifies different types of data through of an optimal hyperplane of decision. On the other hand, Particle Swarm Optimization (PSO) is, in general, an algorithm that finds the best point to represent a dataset. In this paper, PSO is used to find the best data of each class (pattern) to be trained by SVM and there is a comparison of the difference between using or not this optimization. The digits of zero to nine in Brazilian Portuguese language are recognized automatically by SVM. Those digits are pre-processed using mel-cepstral coefficients and Discrete Cosine Transform (DCT) to generate a two-dimensional matrix used as input to the PSO algorithm for generating the optimal data.

引用

页数：6

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