Speech Recognition Method Based on Linear Descending Inertia Weight PSO Algorithm Optimizing SVM Kernel Parameters

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 Gaussian kernel support vector machine optimal parameters (C,¿) based on particle swarm optimization is proposed and a speech recognition system based on support vector machine using the optimal parameters is constructed in this paper. The inertia weight, a crucial parameter of the particle swarm optimization, is adopted in linear descending adjusting method. The speech data is isolated, non-specific and middle vocabulary words. The speech feature we used is modified MFCC feature. Experiments indicate that it is an efficient approach for parameters selection of support vector machine and has higher correct speech recognition rates than default parameters of the support vector machine open source software.