Entropy-aware logistic regression for fusion of large-scale speaker recognition systems

2026-09-20

AI Summary

本文提出了一种基于熵感知的逻辑回归方法，通过结合系统级互补性和话语依赖的不确定性来提高大规模说话人识别系统的融合性能。

Abstract

Score-level fusion based on logistic regression is widely used in speaker recognition to combine complementary systems. However, conventional approaches assign fixed system-dependent coefficients and do not explicitly account for variations in the reliability of individual enrollment and test utterances. Drawing on recent research on the entropy of deep learning-based speaker recognition models, this study incorporates an uncertainty component into the fusion process. By exploiting both system-level complementarity and utterance-dependent uncertainty, the method achieves robust performance in large-scale speaker recognition tasks that involve highly variable characteristics of the speech signal. These results demonstrate that model-entropy information provides a valuable complementary cue in large-scale scenarios.

Problem

• logistic regression
• speaker recognition
• uncertainty
• entropy
• score-level fusion

Innovation

• Entropy-aware
• logistic regression
• score-level fusion
• uncertainty component
• speaker recognition