Endpoint Detection And Initial/Final Segmentation Of Chinese Speech Based On SVM

The speech recognition has a great application prospect in many domains such as the industry, the military, the trade service, the bank service, the medical service, the daily life and so on. Using the support vector machine theory in solving two pivotal techniques in speech recognition, we get a fine effect in endpoint detection and initial/final segmentation.Support vector machine (SVM) is a new method in data mining, and is a new tool for solving machine learning problems in the virtue of optimization methods. It was first proposed by Vapnik in 1990s, and made great progresses in theory research and algorithms application recent years, and is becoming a powerful method to overcome traditional difficulties such as "dimension disaster" and "excessive learning".For endpoint detection based on support vector machine, a new method based on C-support vector machine is proposed in this paper to solve the problem of ado and inaccurateness brought by doors initialization in traditional endpoint detection methods, and this method can also be used to detect and pick out speech segment in which people have special interests.For initial/final segmentation based on support vector machine, traditional initial/final segmentation methods need doors to be initialized first and this needs a great many experimentations and data analysis. We propose a method of initial/final segmentation based on support vector machine which needs no doors to be initialized before segmentation.Researching on the correlation between the classification ability and input character and punishing parameter, and in the condition of extremely lacking of training data, we propose an "eroding method" to process the output of the support vector machine, and so improve the classification precision.