461 citations · 728 across the 27 of their papers we have counts for
5 papers · 1 filter
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data
Mozhdeh Gheini, Tatiana Likhomanenko, Matthias Sperber +1
Self-training has been shown to be helpful in addressing data scarcity for many domains, including vision, speech, and language. Specifically, self-training, or pseudo-labeling, la…
Continuous Soft Pseudo-Labeling in ASR
Tatiana Likhomanenko, Ronan Collobert, Navdeep Jaitly +1
Continuous pseudo-labeling (PL) algorithms such as slimIPL have recently emerged as a powerful strategy for semi-supervised learning in speech recognition. In contrast with earlier…
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…
Flashlight: Enabling Innovation in Tools for Machine Learning
Jacob Kahn, Vineel Pratap, Tatiana Likhomanenko +11
As the computational requirements for machine learning systems and the size and complexity of machine learning frameworks increases, essential framework innovation has become chall…