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Using reverse correlation to uncover the mental representation of one’s own voice| title | Using reverse correlation to uncover the mental representation of one’s own voice |
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| start_date | 2025/07/09 |
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| schedule | 10h30 |
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| online | no |
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| location_info | amphi Jean-Jacques Gagnepain |
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| summary | Most people experience a discrepancy between the sound of their recorded voice (digital self-voice) and the voice they hear while speaking (natural self-voice). This mismatch is often attributed to the fact that natural self-voice perception combines both air and bone conduction, while recorded voices are transmitted through air conduction alone. Although prior studies have attempted to improve self-voice identification through acoustic modifications, they have not identified a generalisable solution and have largely overlooked inter-individual variability. In this talk, I will present findings from a study that used reverse correlation to reveal the perceptual filters individuals rely on to identify their own voice. We recorded 93 participants uttering the syllable [a] and generated hundreds of modified versions of each recording using time-varying filters applied to speech-relevant frequency bands. Participants were then presented with 400 pairs of these modified voices and asked to select the one that best matched how they perceive their voice while speaking. This procedure enabled us to reconstruct each participant’s mental representation of their self-voice. Despite notable inter-individual variability, our results point to a shared filter, characterised by increased amplitude around speech formants (F1–F4), that supports natural self-voice recognition. These findings shed light on the perceptual mismatch between bone- and air-conducted self-voice and may guide the development of more effective voice modification tools, particularly for individuals who depend on text-to-speech communication systems. |
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| responsibles | Aucouturier, Villain |
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Workflow history| from state (1) | to state | comment | date |
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| submitted | published | | 2025/07/01 07:18 UTC |
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