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Recent models such as XLS-R and Whisper have made multilingual speechtechnologies more accessible by pre-training on audio from around 100 spokenlanguages each. However, there are thousands of spoken languages worldwide, andadapting to new languages is an important problem. In this work, we aim tounderstand which model adapts better to languages unseen during pre-training.We fine-tune both models on 13 unseen languages and 18 seen languages. Ourresults show that the number of hours seen per language and language familyduring pre-training is predictive of how the models compare, despite thesignificant differences in the pre-training methods.

Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages