Research On Text-Independent Speaker Recognition

This paper mainly focuses on the study of the basic principles and related algorithms of text-independent speaker recognition. In the endpoint detection, the research is focused on the endpoint detection algorithm. With the shortages of a single parameter which is used for endpoint detection, the combination of energy and Renyi entropy: energy- Renyi entropy parameter is used for endpoint detection. In the speech enhancement, the research is mainly focused on the adaptiveβ?order spectral subtraction. On the basis, the adaptiveβ?orderspectral subtraction based on the iteration of spectral gain function is used to enhance speech spectrum. This algorithm is also applied to the enhancement of the output sub-band energy of CMSBS parameter extraction. Extensive experiments indicate the efficiency of the algorithm. In recognition model, with the study of principles and related algorithms, the research is mainly focused on the SGML algorithm which is used for GMM parameter estimation. The SGML algorithm not only searches the mixed-degree of GMM with self-splitting method but also improves the parameter estimation accuracy on the process of splitting. It is a good solution to the problems that the mixed-degree is difficult decided and EM algorithm is sensitive to the initial value. Furthermore, the performance of the location algorithms is verified on VC platform.