ArtNet: A JEPA-Like Articulatory Predictive Framework for Robust Zero-Shot Phoneme Recognition
arXiv:2606.16595v1 Announce Type: cross Abstract: 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,
The continuous advancements in self-supervised learning for AI and the pursuit of more robust, language-agnostic speech recognition push innovations like ArtNet to address current limitations.
Improving zero-shot, cross-lingual phoneme recognition can significantly reduce the computational and data burden of developing AI models for diverse languages, expanding AI accessibility and utility globally.
The focus on articulatory features as a universal representation could make speech recognition models more robust and less susceptible to language-specific acoustic variations, moving towards more generalized AI.
- · AI developers
- · Multilingual AI applications
- · Developing nations with diverse languages
- · Data-heavy, language-specific ASR solutions
More accurate and efficient AI speech recognition tools for new languages and dialects without extensive retraining.
Accelerated development of voice-controlled interfaces and spoken language understanding systems across various linguistic contexts.
Potential for new forms of human-computer interaction based on universal articulatory patterns, bypassing traditional language barriers.
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Read at arXiv cs.AI