Apple research boosts multilingual speech models via language discrimination pretraining
Apple researchers show that strengthening language discrimination during pretraining reduces the multilingual gap in self-supervised speech models. In a controlled English/French HuBERT setting, two interventions that reinforce language discrimination improved continuous phonetic and higher-level linguistic measures, sometimes closing the gap with monolingual models, while preserving substantial cross-language sharing.
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