Adding an auxiliary language discrimination task to HuBERT pretraining reduces the performance gap between multilingual and monolingual speech models. Researchers at Apple found this method preserves cross-language sharing while improving phonetic accuracy. The approach allows multilingual models to match monolingual benchmarks under the same data budget.