Reconstructing voice identity from noninvasive auditory cortex recordings.

ABSTRACT: The cerebral processing of voice information is known to engage, in human as well as non-human primates, 'temporal voice areas' (TVAs) that respond preferentially to conspecific vocalizations. However, how voice information is represented by neuronal populations in these areas, particularly speaker identity information, remains poorly understood. Here, we used a deep neural network (DNN) to generate a high-level, small-dimension representational space for voice identity-the 'voice latent space' (VLS)-and examined its linear relation with cerebral activity via encoding, representational similarity, and decoding analyses. We find that the VLS maps onto fMRI measures of cerebral activity in response to tens of thousands of voice stimuli from hundreds of different speaker identities and better

SUBMITTER: Lamothe C 

PROVIDER: S-EPMC12782554 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature