改进i-向量说话人识别算法研究

针对信道变化环境下说话人识别系统鲁棒性差及识别率低的问题,提出一种改进i-向量说话人确认算法。首先,利用系统注册说话人GMM-UBM提取话者i-向量;然后,采用加权线性判别分析对i-向量降维和信道补偿,提取更具判别性的特征向量;紧接着,结合类内协方差归一化技术和ZT-norm规整技术对余玄距离得分进行规整,进一步消除信道干扰;最后,构建高鲁棒性余玄距离分类器判定目标说话人。仿真实验结果表明该算法可以有效地提高系统性能。

The research of Improved i-vector Speaker Recognition Algorithm

For the purpose of improving system performance in high channel variability, an improved i-vector speaker verification algorithm is proposed in this paper. Firstly, i-vectors are obtained from GMM-UBM of registered speakers. And then, the weighted linear discriminant analysis is utilized to play the role of channel compensation and dimensionality reduction in i-vectors. By doing this, more discriminant vectors could be extracted. Immediately following, WCCN and ZT-norm are combined to normalize the scores from cosine distance score classifier for the sake of removing channel disturbance. Finally, cosine distance score classifier of high robustness is generated to find target speaker.Experiment results demonstrate that our proposed i-vector system has better performance.