When voices can be cloned: vocal identity threat and occupational self-efficacy among singers in the era of generative AI audio
- 1Department of Music Education and Musicology, Institute of Music, Theatre and Choreography, Herzen State Pedagogical University of Russia , ,
- 2St. Petersburg State Institute of Culture , ,
- 3Beihua University , ,
Abstract
Generative AI voice cloning has advanced rapidly, yet its psychological and professional implications for singers remain underexplored. This review synthesizes empirical evidence concerning two central constructs: vocal identity threat, defined as the perceived erosion of the connection between voice and self, and occupational self-efficacy, referring to confidence in maintaining professional competence and value under technological uncertainty. Neurocognitive and behavioral evidence indicates that the human voice functions as a highly self-relevant identity cue supported by self-prioritized processing, body ownership, and multisensory integration. As AI-generated voice clones become increasingly humanlike, questions have emerged about how such technologies may affect singers' perceptions of vocal identity and professional value. Importantly, perceptual discrimination between human and AI-generated voices remains dependent on stimulus characteristics, listener factors, and the availability of prosodic and expressive cues. We argue that three distinctions are critical for interpreting the current evidence: timbral similarity should not be equated with artistic equivalence, increasingly humanlike output does not establish perceptual interchangeability, and the implications of AI may differ depending on whether it substitutes for identity-bearing vocal labor or scaffolds human-controlled performance and creation. Current evidence therefore provides an empirical basis for investigating potential AI-related threats to singers' vocal identity and occupational confidence, but direct singer-specific evidence remains unavailable. Future research should prioritize singer-specific quantitative studies employing longitudinal designs, ecologically valid voice-cloning scenarios, comparison conditions, and direct measures of identity threat and occupational self-efficacy.
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BibTeX
@article{niu2026when,
title = {{When voices can be cloned: vocal identity threat and occupational self-efficacy among singers in the era of generative AI audio}},
author = {Jingjing Niu and Yinghao Qi and Xiaobin Li},
journal = {Frontiers in psychology},
year = {2026},
month = oct,
doi = {10.3389/fpsyg.2026.1954386},
url = {https://europepmc.org/article/PMC/PMC13720442},
note = {CC BY},
}
RIS
TY - JOUR
TI - When voices can be cloned: vocal identity threat and occupational self-efficacy among singers in the era of generative AI audio
AU - Jingjing Niu
AU - Yinghao Qi
AU - Xiaobin Li
PY - 2026
DA - 2026/10/16
JO - Frontiers in psychology
DO - 10.3389/fpsyg.2026.1954386
UR - https://europepmc.org/article/PMC/PMC13720442
AB - Generative AI voice cloning has advanced rapidly, yet its psychological and professional implications for singers remain underexplored. This review synthesizes empirical evidence concerning two central constructs: vocal identity threat, defined as the perceived erosion of the connection between voice and self, and occupational self-efficacy, referring to confidence in maintaining professional competence and value under technological uncertainty. Neurocognitive and behavioral evidence indicates that the human voice functions as a highly self-relevant identity cue supported by self-prioritized processing, body ownership, and multisensory integration. As AI-generated voice clones become increasingly humanlike, questions have emerged about how such technologies may affect singers' perceptions of vocal identity and professional value. Importantly, perceptual discrimination between human and AI-generated voices remains dependent on stimulus characteristics, listener factors, and the availability of prosodic and expressive cues. We argue that three distinctions are critical for interpreting the current evidence: timbral similarity should not be equated with artistic equivalence, increasingly humanlike output does not establish perceptual interchangeability, and the implications of AI may differ depending on whether it substitutes for identity-bearing vocal labor or scaffolds human-controlled performance and creation. Current evidence therefore provides an empirical basis for investigating potential AI-related threats to singers' vocal identity and occupational confidence, but direct singer-specific evidence remains unavailable. Future research should prioritize singer-specific quantitative studies employing longitudinal designs, ecologically valid voice-cloning scenarios, comparison conditions, and direct measures of identity threat and occupational self-efficacy.
SN - 1664-1078
ER -