Recently, multilingual artificial intelligence assistants, exemplified by
ChatGPT, have gained immense popularity. As a crucial gateway to human-computer
interaction, multilingual automatic speech recognition (ASR) has also garnered
significant attention, as evidenced by systems like Whisper. However, the
proprietary nature of the training data has impeded researchers' efforts to
study multilingual ASR. This paper introduces MSR-86K, an evolving, large-scale
multilingual corpus for speech recognition research. The corpus is derived from
publicly accessible videos on YouTube, comprising 15 languages and a total of
86,300 hours of transcribed ASR data. We also introduce how to use the MSR-86K
corpus and other open-source corpora to train a robust multilingual ASR model
that is competitive with Whisper. MSR-86K will be publicly released on
HuggingFace, and we believe that such a large corpus will pave new avenues for
research in multilingual ASR.

该论文介绍了 MSR-86K，这是一个逐渐增长的大规模多语言语音识别研究语料库，由 YouTube 上公开可访问的视频转录数据组成，包括 15 种语言和总共 86300 小时的 ASR 数据。同时，该论文还介绍了如何使用 MSR-86K 语料库和其他开源语料库来训练一个与 Whisper 相媲美的强大的多语言语音识别模型。我们将在 HuggingFace 上公开发布 MSR-86K，相信这样一个大规模的语料库将为多语言语音识别的研究开辟新的道路。

MSR-86K：一个包含 86,300 小时语音转换文本的多语种演变语料库，用于语音识别研究

MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of  Transcribed Audio for Speech Recognition Research

Paralinguistic traits like cognitive load and emotion are increasingly
recognized as pivotal areas in speech recognition research, often examined
through specialized datasets like CLSE and IEMOCAP. However, the integrity of
these datasets is seldom scrutinized for text-dependency. This paper critically
evaluates the prevalent assumption that machine learning models trained on such
datasets genuinely learn to identify paralinguistic traits, rather than merely
capturing lexical features. By examining the lexical overlap in these datasets
and testing the performance of machine learning models, we expose significant
text-dependency in trait-labeling. Our results suggest that some machine
learning models, especially large pre-trained models like HuBERT, might
inadvertently focus on lexical characteristics rather than the intended
paralinguistic features. The study serves as a call to action for the research
community to reevaluate the reliability of existing datasets and methodologies,
ensuring that machine learning models genuinely learn what they are designed to
recognize.

通过检查专门数据集中的词汇重叠和机器学习模型的性能，我们揭示了特质标签中的显著文本依赖性，结果表明一些机器学习模型可能过多关注词汇特征而不是目标的语音特征，研究呼吁研究界重新评估现有数据集和方法论的可靠性，确保机器学习模型真正学到其设计的内容。