MauBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery

This paper introduces MAUBERT, a multilingual extension of HuBERT that leverages articulatory features for robust cross-lingual phonetic representation learning. We continue Hu-BERT pre-training with supervision based on a phonetic-to-articulatory feature mapping in 55 languages. Our models learn from multilingual data to predict articulatory features or phones, resulting in language-independent representations that capture multilingual phonetic properties. Through comprehensive ABX discriminability testing, we show MAUBERT models produce more context-invariant representations than state-of-the-art multilingual selfsupervised learning models. Additionally, the models effectively adapt to unseen languages and casual speech with minimal self-supervised fine-tuning (10 hours of speech). This establishes an effective approach for instilling linguistic inductive biases in self-supervised speech models. Results. The DiscoPhon results largely corroborate and extend the ABX findings from §5. In zero-shot mode, both MAUBERT variants substantially outperform all baselines across PER, R-value,