关键词: 说话人确认, i-向量, 稀疏表示, 过完备字典, 高斯通用背景模型

Abstract: Sparse representation becomes the research hot because of its excellent classification in speaker verification. The generation of over-complete dictionary is the key problem in sparse representation. This paper proposes a novel sparse representation algorithm based on i-vector and PCA dictionary learning, and applies it to speaker verification. By doing this, it is expected to reduce the noise and channel interference information in over-complete dictionary and improve the robustness of speaker verification. In the method, GMM-UBM is used to extract i-vectors of speakers firstly. And then, WCCN is adopted as channel compensation method to suppress channel interference in i-vectors. According to the mean vectors of i-vector, it estimates channel offset space. In this offset space, it utilizes PCA to obtain channel offset principal components. Using these principal components, it re-computes i-vectors to develop robust over-complete dictionary. In testing phase, it searches sparse representation coefficient vector of the testing i-vectors on this dictionary. Finally, target speaker is judged according to the coefficient vector reconstruction error. Experimental results verify the effectiveness and feasibility of the method.

Key words: speaker verification, i-vector, sparse representation, over-complete dictionary, Gaussian mixture model-universal background model