Abstract
ASR systems exhibit persistent performance disparities across accents, yet the internal mechanisms underlying these gaps remain poorly understood. We introduce ACES, a representation-centric audit that extracts accent-discriminative subspaces and uses them to probe model fragility and disparity. Analyzing Wav2Vec2-base with five English accents, we find that accent information concentrates in a low-dimensional early-layer subspace (layer 3, k=8). Projection magnitude correlates with per-utterance WER (r=0.26), and crucially, subspace-constrained perturbations yield stronger coupling between representation shift and degradation (r=0.32) than random-subspace controls (r=0.15). Finally, linear attenuation of this subspace however does not reduce disparity and slightly worsens it. Our findings suggest that accent-relevant features are deeply entangled with recognition-critical cues, positioning accent subspaces as vital diagnostic tools rather than simple "erasure" levers for fairness.
Abstract (translated)
ASR系统在不同口音之间的性能差异持续存在，但导致这些差距的内部机制仍然不甚明了。我们引入了一种名为ACES的表示中心审计方法，该方法提取出具有口音区分性的子空间，并利用它们来探测模型的脆弱性和差异性。通过分析Wav2Vec2-base在五种英语口音上的表现，我们发现口音信息集中在低维早期层（第三层，k=8）的空间中。投影幅度与每次发音的WER（单词错误率）相关联（r=0.26），更重要的是，在子空间约束下的扰动产生了更强烈的表示变化和性能下降之间的耦合（r=0.32），这比随机选择子空间控制实验的结果（r=0.15）更为显著。最后，线性衰减该子空间并没有减少差异，反而稍微加剧了这种差异。我们的研究结果表明，与识别关键线索紧密相连的口音相关特征是深深交织在一起的，因此将这些口音子空间视为重要的诊断工具而非简单的“消除”手段对于实现公平性而言更为重要。
URL
https://arxiv.org/abs/2603.03359
