标题： 因果解耦对比学习用于多语言说话人嵌入 显示英文标题

标题： Causally Disentangled Contrastive Learning for Multilingual Speaker Embeddings

摘要： 自监督说话人嵌入在说话人验证系统中被广泛使用，但先前的研究表明，它们通常会编码敏感的人口统计属性，引发公平性和隐私问题。 本文研究了在SimCLR训练的说话人嵌入中，人口统计信息（特别是性别、年龄和口音）的呈现程度，以及这种泄露是否可以在不严重降低说话人验证性能的情况下得到缓解。 我们研究了两种去偏策略：通过梯度反转进行对抗训练，以及一种显式分离人口统计信息和剩余信息的因果瓶颈架构。 使用线性和非线性探测分类器来量化人口统计信息的泄露，而说话人验证性能则通过ROC-AUC和EER进行评估。 我们的结果表明，性别信息在基线嵌入中被强烈且线性地编码，而年龄和口音较弱，主要以非线性方式表示。 对抗去偏减少了性别泄露，但对年龄和口音的影响有限，并且与验证准确率存在明显的权衡。 因果瓶颈进一步抑制了人口统计信息，尤其是在剩余表示中，但导致了显著的性能下降。 这些发现突显了在自监督说话人嵌入中减轻人口统计信息泄露的基本局限性，并阐明了当前去偏方法中固有的权衡。 显示英文摘要

摘要： Self-supervised speaker embeddings are widely used in speaker verification systems, but prior work has shown that they often encode sensitive demographic attributes, raising fairness and privacy concerns. This paper investigates the extent to which demographic information, specifically gender, age, and accent, is present in SimCLR-trained speaker embeddings and whether such leakage can be mitigated without severely degrading speaker verification performance. We study two debiasing strategies: adversarial training through gradient reversal and a causal bottleneck architecture that explicitly separates demographic and residual information. Demographic leakage is quantified using both linear and nonlinear probing classifiers, while speaker verification performance is evaluated using ROC-AUC and EER. Our results show that gender information is strongly and linearly encoded in baseline embeddings, whereas age and accent are weaker and primarily nonlinearly represented. Adversarial debiasing reduces gender leakage but has limited effect on age and accent and introduces a clear trade-off with verification accuracy. The causal bottleneck further suppresses demographic information, particularly in the residual representation, but incurs substantial performance degradation. These findings highlight fundamental limitations in mitigating demographic leakage in self-supervised speaker embeddings and clarify the trade-offs inherent in current debiasing approaches.