most citedSome voices are too common: Building fair speech recognition systems using the Common Voice dataset

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2023

Compression of Recurrent Neural Networks using Matrix Factorization

Lucas Maison, Hélion du Mas des Bourboux, Thomas Courtat

Compressing neural networks is a key step when deploying models for real-time or embedded applications. Factorizing the model's matrices using low-rank approximations is a promisin…

eess.AS2023★ 1 cited

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…

cs.LG2023

Improving Accented Speech Recognition with Multi-Domain Training

Lucas Maison, Yannick Estève

Thanks to the rise of self-supervised learning, automatic speech recognition (ASR) systems now achieve near-human performance on a wide variety of datasets. However, they still lac…