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Related Concept Videos

Auditory Perception01:17

Auditory Perception

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The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
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Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Reconstructing voice identity from noninvasive auditory cortex recordings.

Charly Lamothe1,2, Etienne Thoret1,2,3,4, Régis Trapeau1

  • 1La Timone Neuroscience Institute UMR 7289, CNRS, Aix-Marseille University, Marseille, France.

Elife
|January 8, 2026
PubMed
Summary
This summary is machine-generated.

Researchers mapped voice identity using a deep neural network's voice latent space (VLS). This VLS effectively represents speaker identity in the temporal voice areas (TVAs), enabling voice reconstructions.

Keywords:
DNNscomputational biologyfMRIhumanneurosciencesystems biologytemporal voice areasvoice perceptionvoice reconstruction

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Area of Science:

  • Neuroscience
  • Auditory Perception
  • Machine Learning in Neuroscience

Background:

  • Temporal voice areas (TVAs) process conspecific vocalizations in primates.
  • Neural representation of speaker identity within TVAs is not well understood.

Purpose of the Study:

  • To investigate how speaker identity is represented in the brain.
  • To explore the utility of a deep neural network-derived voice latent space (VLS) for understanding neural representations of voice identity.

Main Methods:

  • Utilized a deep neural network (DNN) to create a voice latent space (VLS).
  • Employed encoding, representational similarity, and decoding analyses to relate VLS to fMRI data.
  • Analyzed responses to thousands of voice stimuli from hundreds of speakers.

Main Results:

  • The VLS linearly mapped onto fMRI activity in temporal voice areas (TVAs).
  • VLS better explained representational geometry for speaker identity in TVAs compared to primary auditory cortex (A1).
  • TVA-based reconstructions using VLS preserved speaker identity.

Conclusions:

  • The DNN-derived VLS provides high-level representations of voice identity in TVAs.
  • This study advances understanding of neural coding for speaker recognition.