| dc.description.abstract | Speaker recognition has always been a popular topic in speech recognition research, and is applied in many area. Here, we take ＂Access Control System＂ as one of the applications. Currently, i-vector based speaker recognition system has achieved great performance. On the other hand, there are many researches concentrating on Sparse Representation Classifier (SRC). We thus base our system on those two novel concepts, i-vector and SRC, and propose some method to improve the system.
In respect of feature extraction, we construct a Supervector with Probability Principal Component Analysis (PPCA), and choose the number of eigenvalues by bartlett test, so that we can select appropriate dimension for each components. In the second part of the system, we enhance the sparse dictionary, which includes choosing primary elements of the dictionary, compensating session and channel variability, and making the dictionary discriminative. In the third part, we propose noise dictionary by collecting the noise of Robust PCA, Nuisance Attribute Projection (NAP) and Joint Factor Analysis (JFA). We believe that noise basis can absorb some variability and achieve the effect of de-noising. Finally, we solve sparse coefficients using Approximate Bayesian Compressed Sensing (ABCS), which is a bayesian probability method, and restrict the sparse coefficients by assuming them being Semi-Gaussian distribution.
Experimental results verify that the selected features, the dictionary processing, as well as the method for solving coefficients, has given improvement to the recognition rate up to a certain extent. | en_US |