Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation
  over More Languages and Beyond
Jiatong Shi William Chen Dan Berrebbi Hsiu-Hsuan Wang Wei-Ping Huang En-Pei Hu Ho-Lam Chuang Xuankai Chang Yuxun Tang Shang-Wen Li Abdelrahman Mohamed Hung-yi Lee Shinji Watanabe
The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB)
Challenge expands upon the acclaimed SUPERB framework, emphasizing
self-supervised models in multilingual speech recognition and language
identification. The challenge comprises a research track focused on applying
ML-SUPERB to specific multilingual subjects, a Challenge Track for model
submissions, and a New Language Track where language resource researchers can
contribute and evaluate their low-resource language data in the context of the
latest progress in multilingual speech recognition. The challenge garnered 12
model submissions and 54 language corpora, resulting in a comprehensive
benchmark encompassing 154 languages. The findings indicate that merely scaling
models is not the definitive solution for multilingual speech tasks, and a
variety of speech/voice types present significant challenges in multilingual
speech processing.