6 citations · 7 across the 10 of their papers we have counts for
4 papers · 1 filter
Avoid Overthinking in Self-Supervised Models for Speech Recognition
Dan Berrebbi, Brian Yan, Shinji Watanabe
Self-supervised learning (SSL) models reshaped our approach to speech, language and vision. However their huge size and the opaque relations between their layers and tasks result i…
More Speaking or More Speakers?
Dan Berrebbi, Ronan Collobert, Navdeep Jaitly +1
Self-training (ST) and self-supervised learning (SSL) methods have demonstrated strong improvements in automatic speech recognition (ASR). In spite of these advances, to the best o…
Continuous Pseudo-Labeling from the Start
Dan Berrebbi, Ronan Collobert, Samy Bengio +2
Self-training (ST), or pseudo-labeling has sparked significant interest in the automatic speech recognition (ASR) community recently because of its success in harnessing unlabeled…
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation
Dan Berrebbi, Jiatong Shi, Brian Yan +3
Self-Supervised Learning (SSL) models have been successfully applied in various deep learning-based speech tasks, particularly those with a limited amount of data. However, the qua…