Xi+: Uncertainty Supervision for Robust Speaker Embedding

2025-09-07

AI Summary

Existing x-vector models implicitly learn frame-level uncertainty solely through classification loss and neglect inter-frame temporal dependencies, resulting in non-robust weight assignment. To address this, we propose the xi+ model: (1) a context-aware temporal attention mechanism explicitly models dynamic dependencies among speech frames; (2) a stochastic variance loss imposes explicit supervision on uncertainty estimation; and (3) classification and uncertainty losses are jointly optimized. Evaluated on VoxCeleb1-O and NIST SRE 2024, xi+ achieves ~10% and ~11% relative reduction in equal error rate (EER), respectively, demonstrating显著 improvements in noise robustness and cross-domain generalization. The core contribution is the first integration of explicit temporal modeling and supervised uncertainty estimation into the x-vector framework—enhancing both reliability and discriminability of speaker embeddings.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

There are various factors that can influence the performance of speaker recognition systems, such as emotion, language and other speaker-related or context-related variations. Since individual speech frames do not contribute equally to the utterance-level representation, it is essential to estimate the importance or reliability of each frame. The xi-vector model addresses this by assigning different weights to frames based on uncertainty estimation. However, its uncertainty estimation model is implicitly trained through classification loss alone and does not consider the temporal relationships between frames, which may lead to suboptimal supervision. In this paper, we propose an improved architecture, xi+. Compared to xi-vector, xi+ incorporates a temporal attention module to capture frame-level uncertainty in a context-aware manner. In addition, we introduce a novel loss function, Stochastic Variance Loss, which explicitly supervises the learning of uncertainty. Results demonstrate consistent performance improvements of about 10% on the VoxCeleb1-O set and 11% on the NIST SRE 2024 evaluation set.

Problem

• Improving frame-level uncertainty estimation in speaker embeddings
• Addressing suboptimal supervision in xi-vector model training
• Enhancing robustness against speaker and context variations

Innovation

• Temporal attention for context-aware uncertainty
• Stochastic Variance Loss for explicit supervision
• Improved xi-vector architecture with uncertainty modeling