Some voices are too common: Building fair speech recognition systems
  using the Common Voice dataset
Lucas Maison Yannick Estève
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.