Apple research says stronger language discrimination narrows the multilingual speech gap
Authors' own tests show phone discrimination beats the monolingual baseline while lexical measures still trail, limited to an English-French setup.
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Apple's machine learning research team reports that strengthening a model's ability to discriminate languages during pretraining narrows the gap between multilingual speech models and monolingual ones.
In a controlled English-French HuBERT setup, adding an auxiliary language classifier or per-language clustering targets cut phone discrimination error from 11.6% to 10.4%, below the monolingual model's 10.8%.
Lexical performance rose from 52.1% to 56.7%, still behind the monolingual 58.5%. These are the authors' own controlled experiments, and the findings are limited to the English-French setting.