The authors trained HuBERT models on English and French speech with a fixed total data budget, comparing a standard bilingual baseline against two interventions that enhance language discrimination: an auxiliary language classifier and per-language k-means targets.
Language discrimination training reduces the multilingual gap in speech models
Adding a language classifier or language-specific targets during pretraining helps bilingual HuBERT match monolingual phone, lexical, and prosodic performance.
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Maureen de Seyssel · Jie Chi · Zakaria Aldeneh
Apple
Research Digest··2 min read
de Seyssel et al.
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Strengthening language discrimination during pretraining closes the multilingual gap in speech models.
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