Perceptual Alignment in Audio‑Native AI Systems: Evaluating Prosodic Signals for Trust and Safety in Voice‑Based Interaction

Perceptual Alignment in Audio‑Native AI Systems: Evaluating Prosodic Signals for Trust and Safety in Voice‑Based Interaction introduces “perceptual alignment” as a missing evaluation layer for voice‑native AI. As conversational agents move from text‑rendered speech to audio‑native interaction, users increasingly rely on tone, cadence, hesitation, and vocal authority - not just words - to decide whether an AI system is cautious, confident, or trustworthy. Yet most current evaluation pipelines still focus on sensory fidelity (intelligibility, naturalness, speaker similarity) and largely ignore how prosody may misrepresent a model’s underlying epistemic state.

This white paper defines the Perceptual Alignment Gap: the space where prosodic signals of confidence, empathy, or authority diverge from what the model “actually knows.” It proposes Tonal Contamination as an umbrella failure mode for over‑confident, over‑agreeable, or context‑inappropriate tone (e.g., tonal hallucination, tonal sycophancy, authority miscalibration, tonal trust drift, ambivalence blindness), and argues that these behaviors have direct implications for safety red‑teaming, scalable oversight, and regulatory compliance in voice‑based deployments.

Building on the Tonality as Attention framework and the TonalityPrint perceptual reference dataset, the paper introduces Perceptual Alignment Error (PAE) as a conceptual measure of the gap between listener‑perceived confidence and a model’s own reported uncertainty, alongside related indices such as a Contextual Authority Index (CAI) and Cross‑Modal Congruence Score (CCS). It outlines a dual‑track evaluation pipeline in which perceptual alignment runs in parallel with existing acoustic and semantic tests, providing an observable “trust surface” for monitoring voice agents in high‑stakes domains e.g., healthcare, finance, legal review, and autonomous systems.

This work is intentionally framed as a measurement agenda rather than a finalized benchmark. It is written for researchers, safety teams, and organizations building or deploying audio‑native AI who need language, concepts, and early tooling directions to reason about how AI voices signal certainty, uncertainty, trustworthiness and intent. Perceptual Alignment is proposed as a pre‑competitive evaluation challenge - an invitation to collaborate on standards, datasets, and red‑teaming practices that make prosodic behavior in AI systems more measurable, auditable, and aligned with human expectations of epistemic honesty.