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.

2023 年多语言演讲通用性能基准（ML-SUPERB）挑战扩展了备受赞誉的 SUPERB 框架，强调自监督模型在多语音识别和语言识别中的应用。挑战包括一个研究轨道，重点是将 ML-SUPERB 应用于特定的多语言主题，一个挑战轨道，用于模型提交，以及一个新语言轨道，在这个轨道上，语言资源研究人员可以贡献和评估他们的低资源语言数据，以适应多语音识别的最新进展。研究结果表明，仅仅提高模型规模并非多语音任务的终极解决方案，多种语音 / 语调类型在多语音处理中存在重大挑战。