Wav2Vec 2.0 Learns Brain-Like Representations From Just 600 Hours of Unlabeled Speech Data in New Study

In the new paper Toward a Realistic Model of Speech Processing in the Brain with Self-supervised Learning, researchers show that self-supervised architectures such as Wav2Vec 2.0 can learn brain-like representations from as little as 600 hours of unlabelled speech; and can also learn sound-generic and speech- and language-specific representations similar to those of the prefrontal and temporal cortices.

ai artificial intelligence brain data deep-neural-networks machine learning machine learning & data science ml research self-supervised learning speech speech processing study technology