标题： 一些声音过于常见：使用Common Voice数据集构建公平的语音识别系统 显示英文标题

标题： Some voices are too common: Building fair speech recognition systems using the Common Voice dataset

摘要： 自动语音识别（ASR）系统由于神经网络训练的新进展，如自监督学习，变得越来越高效。 然而，它们对某些群体是不公平的，例如有口音的人。 在这项工作中，我们使用法语Common Voice数据集来量化预训练的wav2vec~2.0模型对多个人口统计群体的偏见。 通过在各种固定大小、精心设计的训练集上微调预训练模型，我们展示了说话人多样性的的重要性。 我们还对Common Voice语料库进行了深入分析，并确定了用户在使用该数据集时应考虑的重要缺陷。 显示英文摘要

摘要： Automatic speech recognition (ASR) systems become increasingly efficient thanks to new advances in neural network training like self-supervised learning. However, they are known to be unfair toward certain groups, for instance, people speaking with an accent. In this work, we use the French Common Voice dataset to quantify the biases of a pre-trained wav2vec~2.0 model toward several demographic groups. By fine-tuning the pre-trained model on a variety of fixed-size, carefully crafted training sets, we demonstrate the importance of speaker diversity. We also run an in-depth analysis of the Common Voice corpus and identify important shortcomings that should be taken into account by users of this dataset.