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