From Voice to Self: An Integrative Framework on Self-Voice Processing

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Abstract
The self-voice plays a fundamental role in communication and identity yet remains a relatively neglected topic in psychological science. As AI-generated and digitally manipulated voices become more common, understanding how individuals perceive and process their own voice is increasingly important. Disruptions in self-voice processing are implicated in several clinical conditions, including psychosis, autism, and personality disorders, highlighting the need for integrative models to explain the self-voice across contexts. However, research faces two major challenges: a methodological one (i.e., replicating the bone-conducted acoustics that shape natural self-voice perception) and a conceptual one (i.e., a persistent bias toward treating the self-voice as purely auditory). To address these gaps, we propose a framework that decomposes the self-voice into five interacting components: auditory, motor control, memory, multisensory integration, and self-concept. We review the functional and neural basis of each component and suggest how they converge within distributed brain networks to support coherent self-voice processing. This integrative framework aims to advance theoretical and translational work by bridging psychology, neuroscience, clinical research, and voice technology in the context of emerging digital voice environments.
Funding Information
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (CRSK-1_227695)
  • Fundação para a Ciência e a Tecnologia (2023.00041.RESTART)
  • Fundação Bial (BIAL 146/2020)

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