Zero-shot cross-lingual phoneme recognition is often hindered by the fragility of direct acoustic-to-symbol mapping, which is susceptible to language-specific variations. Echoing joint-embedding predictive architecture (JEPA) work in vision, we propose ArtNet, a framework that explores a structured feature prediction task based on articulatory features to enhance acoustic robustness. Specifically, ArtNet integrates an articulatory predictor, designed to extract universal articulatory representations from self-supervised learning (SSL) features, with a variational information bottleneck (VIB) to suppress language-specific variations. Experiments on seven unseen languages demonstrate that ArtNet, particularly when synergized with the proposed vector-space inventory alignment (VSIA) strategy, significantly outperforms competitive baselines, achieving a 20.56\% relative reduction in phoneme error rate (PER) and 7.01\% in phoneme feature error rate (PFER).

翻译：零样本跨语言音素识别常因直接声学到符号映射的脆弱性而受阻，这种映射易受语言特异性变化影响。借鉴视觉领域中的联合嵌入预测架构（JEPA）工作，我们提出ArtNet框架，该框架探索基于发音特征的机构化特征预测任务以增强声学鲁棒性。具体而言，ArtNet集成了一个发音预测器（用于从自监督学习特征中提取通用发音表征）与变分信息瓶颈（VIB）以抑制语言特异性变化。在七种未见语言上的实验表明，ArtNet在协同所提出的向量空间清单对齐（VSIA）策略时显著优于竞争基线，实现了音素错误率（PER）20.56%和音素特征错误率（PFER）7.01%的相对降低。