  Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard
to benchmark the performance of Self-Supervised Learning (SSL) models on
various speech processing tasks. However, SUPERB largely considers English
speech in its evaluation. This paper presents multilingual SUPERB (ML-SUPERB),
covering 143 languages (ranging from high-resource to endangered), and
considering both automatic speech recognition and language identification.
Following the concept of SUPERB, ML-SUPERB utilizes frozen SSL features and
employs a simple framework for multilingual tasks by learning a shallow
downstream model. Similar to the SUPERB benchmark, we find speech SSL models
can significantly improve performance compared to FBANK features. Furthermore,
we find that multilingual models do not always perform better than their
monolingual counterparts. We will release ML-SUPERB as a challenge with
organized datasets and reproducible training scripts for future multilingual
representation research.
