Human and AI voice identities evoke shared neural signatures during speaker recognition across changes in speech content and prosody

Abstract

Both biologically-produced human voices and algorithmically-generated AI speech manifest speaker identity. Critically, prosodic variations modulate the acoustic dimensions (e.g., fundamental frequency) that also shape individual speaker identity representations. So far it remains unclear whether listeners process speaker identities in human and AI voices through neurologically equivalent mechanisms, nor how prosodic cues might influence these cognitive processes. We examined event-related potentials during old/new speaker discrimination after name-based identity learning, and further analyzed correctly recognized old speakers comparing trials where prosody matched vs. mismatched between learning and testing. For old/new discrimination, multivariate pattern analysis (MVPA) revealed three significant late windows (662-1498 ms) with Pz as the primary contributor for AI voices, yet none for human voices. Univariate analyses revealed that human voices showed earlier widespread discrimination (N250: 200-280 ms), while both voice types converged on Pz as the strongest contributor based on effect size rankings for late old/new effects (400-800 ms). These old/new effects emerged across completely different speech content between learning and testing, addressing a gap in prior literature. For speaker-specific prosodic expectation effects in the 500-900 ms window, unexpected prosody elicited late positivity for human voices compared to the prosody used during learning, whereas AI voices elicited late negativity. The late positivity resembles P600 components observed for communicative style expectancy violations, while the late negativity likely reflects effortful reprocessing of prosodic violations within atypical synthetic signals, analogous to accented speech processing. Our study advances understanding of voice identity in cognitive neuroscience and offers implications for AI voices in human-computer interaction. [Word count: 250]