ML-SUPERB evaluates self-supervised learning (SSL) models on the tasks of
language identification and automatic speech recognition (ASR). This benchmark
treats the models as feature extractors and uses a single shallow downstream
model, which can be fine-tuned for a downstream task. However, real-world use
cases may require different configurations. This paper presents ML-SUPERB~2.0,
which is a new benchmark for evaluating pre-trained SSL and supervised speech
models across downstream models, fine-tuning setups, and efficient model
adaptation approaches. We find performance improvements over the setup of
ML-SUPERB. However, performance depends on the downstream model design. Also,
we find large performance differences between languages and datasets,
suggesting the need for more targeted approaches to improve multilingual ASR
performance.

ML-SUPERB~2.0 是一个新的基准系统，用于评估预训练的自监督学习和监督语音模型在下游模型、微调设置和高效模型适应方法方面的性能，它发现了 ML-SUPERB 设置的性能改进，但性能取决于下游模型设计，并且在语言和数据集之间存在大的性能差异，表明需要更有针对性的方法来改进多语言 ASR 性能。

ML-SUPERB 2.0：跨模型约束、语言和数据集进行多语言语音模型基准测试

ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling  Constraints, Languages, and Datasets

Taiwanese Hokkien is declining in use and status due to a language shift
towards Mandarin in Taiwan. This is partly why it is a low resource language in
NLP and speech research today. To ensure that the state of the art in speech
processing does not leave Taiwanese Hokkien behind, we contribute a 1.5-hour
dataset of Taiwanese Hokkien to ML-SUPERB's hidden set. Evaluating ML-SUPERB's
suite of self-supervised learning (SSL) speech representations on our dataset,
we find that model size does not consistently determine performance. In fact,
certain smaller models outperform larger ones. Furthermore, linguistic
alignment between pretraining data and the target language plays a crucial
role.

台語的使用和地位因台灣向普通話的語言轉變而下降。這是目前自然語言處理和語音研究中它成為低資源語言的部分原因。為了確保台語不落後於語音處理技術的最新發展，我們向 ML-SUPERB 的隱藏集合中貢獻了一個 1.5 小時的台語資料集。在我們的資料集上評估 ML-SUPERB 的自我監督學習（SSL）語音表示套件，發現模型大小並不一致地決定性能。實際上，某些較小的模型表現更好。此外，預訓練數據和目標語言之間的語言對齊起關鍵作用。

评估基于自我监督的语音模型在一个台湾福建语语料库上的表现

Evaluating Self-supervised Speech Models on a Taiwanese Hokkien Corpus

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。