基于i-向量和PCA字典学习稀疏表示的说话人确认

稀疏表示以其出色的分类性能成为说话人确认研究的热点，其中过完备字典的构建是关键，直接影响其性能。为了提高说话人确认系统的鲁棒性，同时解决稀疏表示过完备字典中存在噪声及信道干扰信息的问题，提出一种基于i-向量的主成分稀疏表示字典学习算法。该算法在高斯通用背景模型的基础上提取说话人的i-向量，并使用类内协方差归一化技术对i-向量进行信道补偿；根据信道补偿后的说话人i-向量的均值向量估计其信道偏移空间，在该空间采用主成分分析方法提取低维信道偏移主分量，用于重新计算说话人i-向量，从而达到进一步抑制i-向量中信道干扰的目的；将新的i-向量作为字典原子构建高鲁棒性稀疏表示过完备字典。在测试阶段，测试语音的i-向量在该字典上寻找其稀疏表示系数向量，根据系数向量对测试i-向量的重构误差确定目标说话人。仿真实验表明，该算法具有良好的识别性能。

Speaker verification based on i-vector and sparse representation using PCA dictionary learning

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.