Toward a realistic model of speech processing in the brain with self-supervised learning

Understand

Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input.

• These algorithms, however, remain largely implausible: they require (1) extraordinarily large amounts of data, (2) unobtainable supervised labels, (3) textual rather than raw sensory input, and / or (4) implausibly large memory (e.g.
• thousands of contextual words).
• These elements highlight the need to identify algorithms that, under these limitations, would suffice to account for both behavioral and brain responses.