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.

本文提出的多语种 SUPERB (ML-SUPERB) 利用冻结的自我监督学习 (SSL) 特征和简单的框架，考虑了 143 种语言（从高资源到濒危），并考虑了自动语音识别和语言识别。我们发现语音 SSL 模型与 FBANK 特征相比可以显著提高性能，但我们发现多语种模型并不总是比它们的单语种模型表现更好。我们将发布 ML-SUPERB 作为挑战，以组织数据集和可复制的训练脚本，用于未来的多语种表示研究。