Some voices are too common: Building fair speech recognition systems using the Common Voice dataset
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 29 of its 35 references that are in the map. First posted on arXiv 1 Jun 2023.
Builds on
29 of 35 references.
References · 29
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
- 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
- 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
- 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,380
- 2021VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretationcited by 463
- 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 406
- 2021W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Trainingcited by 347
- 2022Self-Supervised Speech Representation Learning: A Reviewcited by 260
17 more
- 2023Google USM: Scaling Automatic Speech Recognition Beyond 100 Languagescited by 231
- 2020Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognitioncited by 187
- 2021Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Trainingcited by 151
- 2020Improved Noisy Student Training for Automatic Speech Recognitioncited by 141
- 2020Self-training and Pre-training are Complementary for Speech Recognitioncited by 99
- 2021BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognitioncited by 88
- 2020Rethinking Evaluation in ASR: Are Our Models Robust Enough?cited by 73
- 2022How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communicationscited by 30
- 2021Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptionscited by 26
- 2022A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognitioncited by 22
- 2022DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASRcited by 21
- 2021Don't speak too fast: The impact of data bias on self-supervised speech modelscited by 20
- 2022A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systemscited by 15
- 2021ASR4REAL: An extended benchmark for speech modelscited by 14
- 2021Accented Speech Recognition: A Surveycited by 12
- 2022Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translationcited by 11
- 2022Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidationcited by 4
Built on by
Citing papers per year: 2025: 1; 2026: 2.
3 papers.