A Unified Uncertainty-Aware Back-End for Speaker Verification: Scoring, Normalization, and Calibration

2026-09-01

AI Summary

论文提出了一种统一的不确定性感知后端，通过结合不确定性信息改进说话人验证中的评分、归一化和校准步骤，从而提高系统性能。

Abstract

Speaker verification back-ends commonly combine similarity scoring, score normalization, and calibration. However, speaker embeddings extracted from real-world utterances have trial-dependent reliability because of factors such as duration, noise, and channel variation. Existing uncertainty-aware methods primarily improve the speaker encoder or the initial similarity score, while the estimated uncertainty is typically not propagated through subsequent normalization and calibration. We represent each utterance by a speaker embedding, interpreted as a posterior mean, together with its covariance as an uncertainty estimate. We present a unified uncertainty-aware back-end comprising uncertainty-aware cosine scoring, uncertainty-aware AS-Norm (UAS-Norm), and uncertainty-aware Quality Measure Function calibration (UQMF). Covariance information is incorporated throughout this pipeline to adjust score scaling, cohort statistics, normalized-score combination, and calibration features. Experiments with ECAPA-TDNN and ResNet show consistent EER reductions and improved target--non-target separation across both architectures.

Problem

• speaker verification
• uncertainty-aware
• normalization
• calibration

Innovation

• uncertainty-aware
• speaker verification
• score normalization
• calibration
• covariance