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

This paper introduces Multilingual LibriSpeech (MLS) dataset, a large
multilingual corpus suitable for speech research. The dataset is derived from
read audiobooks from LibriVox and consists of 8 languages, including about
44.5K hours of English and a total of about 6K hours for other languages.
Additionally, we provide Language Models (LM) and baseline Automatic Speech
Recognition (ASR) models and for all the languages in our dataset. We believe
such a large transcribed dataset will open new avenues in ASR and
Text-To-Speech (TTS) research. The dataset will be made freely available for
anyone at this http URL

本文介绍了 Multilingual LibriSpeech（MLS）数据集，这是一个大型的多语言语音研究语料库，包括 8 种语言，以及相应语言模型和基线自动语音识别模型。该数据集将在 http URL 上免费提供，我们相信它将为 ASR 和 TTS 研究开辟新的途径。