  Modern multilingual automatic speech recognition (ASR) systems like Whisper
have made it possible to transcribe audio in multiple languages with a single
model. However, current state-of-the-art ASR models are typically evaluated on
individual languages or in a multi-task setting, overlooking the challenge of
continually learning new languages. There is insufficient research on how to
add new languages without losing valuable information from previous data.
Furthermore, existing continual learning benchmarks focus mostly on vision and
language tasks, leaving continual learning for multilingual ASR largely
unexplored. To bridge this gap, we propose CL-MASR, a benchmark designed for
studying multilingual ASR in a continual learning setting. CL-MASR provides a
diverse set of continual learning methods implemented on top of large-scale
pretrained ASR models, along with common metrics to assess the effectiveness of
learning new languages while addressing the issue of catastrophic forgetting.
To the best of our knowledge, CL-MASR is the first continual learning benchmark
for the multilingual ASR task. The code is available at
https://github.com/speechbrain/benchmarks.
