Emotional variability analysis based I-vector for speaker verification in under-stress conditions

Abstract

Emotional conditions cause changes in the speech production system. It produces the differences in the acoustical characteristics compared to neutral conditions. The presence of emotion makes the performance of a speaker verification system degrade. In this paper, we propose a speaker modeling that accommodates the presence of emotions on the speech segments by extracting a speaker representation compactly. The speaker model is estimated by following a similar procedure to the i-vector technique, but it considerate the emotional effect as the channel variability component. We named this method as the emotional variability analysis (EVA). EVA represents the emotion subspace separately to the speaker subspace, like the joint factor analysis (JFA) model. The effectiveness of the proposed system is evaluated by comparing it with the standard i-vector system in the speaker verification task of the Speech Under Simulated and Actual Stress (SUSAS) dataset with three different scoring methods. The evaluation focus in terms of the equal error rate (EER). In addition, we also conducted an ablation study for a more comprehensive analysis of the EVA-based i-vector. Based on experiment results, the proposed system outperformed the standard i-vector system and achieved state-of-the-art results in the verification task for the under-stressed speakers.