Parameters Optimization And Application Of SVM Based On AFSA

Speech recognition is an important aspect of speech signal processing. It is the foundation of human-computer interaction technology and has wide application prospect. Automatic speech recognition is essentially a problem of pattern multi-class classification, so the SVM classifier which is well adapted to high-dimensional classification problems is applied in the speech recognition quickly. Support Vector Machine is a high-performance learning machine on the basis of the statistical learning theory. However there is a problem in SVM that it depends on the performance of the parameters settings, including penalties and kernel parameters, but no suitable theory can guide to find adapted parameters. The parameters in SVM model are analyzed, the SVM model parameters are optimized by Artificial Fish Swarm Algorithm, and a SVM parameters optimization method based on Artificial Fish Swarm Algorithm.This paper first introduced the basic principle of support vector machine and the existing typical types systematically, analyzed the influence of the parameters for the SVM model and used AFSA to optimize the SVM parameters. In order to verify the recognition effect of support vector machine based on AFSA in speech recognition system, this paper constructed four non-specific person and isolated words speech recognition system which is based on support vector machines of radial basis kernel function and did a lot of simulation experiments. The experimental results show that the recognition results of speech recognition system which is based on radial basis kernel support vector machine based on AFSA is very good and better than the recognition results that is based on hidden markov model.Secondly, in order to improve the recognition results of the constructed model, this paper studied AFSA and did some improvement using the Niche Technology to maintain the diversity of the sample data. At the same time, the common testing functions were used to test the performance of the improved algorithm and achieved satisfactory results. This paper constructed a non-specific person and isolated words speech recognition system based on support vector machine of radial basis kernel function. In the experiments, the recognition results of speech recognition system which is based on radial basis kernel support vector machine based on improved AFSA is very good. The experimental results show that the different values of kernel parameter and error penalty parameters affect the generalization performance of support vector machine and accordingly affect the recognition effect of the speech recognition system.The selection of kernel function type, kernel parameter value and error penalty parameter value directly affects the recognition effect of speech recognition system based on support vector machine. However, there is no scientific method to select these three factors and people select them only according to experience and repeated experiments. There exists great limitation. Aiming at this problem, this paper did preliminary research and proposed a method to do parameters optimization that uses artificial fish swarm algorithm in condition of the kernel function type is fixed. At last this paper constructed a speech recognition system based on the support vector machine whose kernel parameter and error penalty parameter have been optimized and the recognition rates get certain improvement.