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。

ML-SUPERB: 多语言语音通用性能基准