Speaker Recognition Technology, Based On Independent Component Analysis

With the development of Information Technology, the secure authentication becomes more important than ever. Biometrics is a new identification approach and it shows advantages in this area. Speaker recognition, which identifies or verifies people by their voice, is regarded as the most natural and convenient one among the methods of biometrics. However, there are still many problems when we want to apply speaker recognition to real applications. One is the long computational time of training a speaker model or test an utterance, which makes Real-time implementation very hard and expensive. In this thesis, based on the study of the recent advancements and main points of speaker recognition, we tried to solve the problem by using two methods of Multivariate Statistical Analysis: Principal Component Analysis (PCA) and Independent Component Analysis(ICA). This thesis proposed a new text-independent speaker identification technique. A popular decomposition called principal component analysis (PCA) is widely used for feature extraction. PCA has the ability to find out the principal components which are mutually orthogonal. We may also reduce the dimension of feature through these principal components. In the beginning, we construct a time-frequency covariance matrix using the original feature extracted from each frame. Then use PCA to obtain a transformation matrix to get better feature. However, the identification rate is not very good. So we propose some approaches to improve the performance. At first, we propose another decomposition called independent component analysis (ICA). ICA is more and more popular in recent year because it ability to find out the independent components which are mutually independent. Using ICA, we get better identification rate than using PCA.