基于VEMAP的说话人识别鲁棒性研究

为了改善发声力度变化对说话人识别系统性能的影响.针对不同发声力度下语音信号的分析,提出了使用发声力度最大后验概率(Vocal Effort Maximum A Posteriori,VEMAP)自适应方法更新基于高斯混合模型-通用背景模型(Gaussian Mixture Model-Universal Background Model,GMM-UBM)的说话人识别系统模型.实验表明,所提出的方法使不同发声力度下系统EER％降低了88.45％与85.16％,有效解决了因发声力度变化引起的训练语音与测试语音音量失配,从而导致说话人识别性能降低的问题,改善说话人识别系统性能效果显著.

Research on robustness of Speaker Recognition Based on VEMAP

In order to improve the performance of recognition system caused which is influenced by the changes of vocal efforts. In this paper, based on the analysis of the speech signals under different vocal efforts ,Vocal Effort Maximum a posteriori(VEMAP) adaptive method has been proposed to update the speaker recognition model which based on Gaussian Mixture Model-Universal Background Model (GMM-UBM). From the results of this experiment, VEMAP adaptive method can make the EER% of recognition system which under different vocal efforts reduce by 88.45% and 85.16%. Effectively solve the vocal mismatch of training speech and test speech caused by vocal effort that lead to the Speaker recognition performance degradation problem, Improving the speaker recognition system performance obviously.