Perceptual Alignment
is the missing evaluation layer
in audio-native AI

Perceptual alignment is the degree to which an audio-native AI system's vocal output accurately conveys its intended meaning, emotional register, and authority level to a human listener. It concerns how AI systems sound - not just what they say - and whether that sound creates accurate perceptions of competence, certainty, empathy, and intent.

Conventional alignment focuses on whether AI systems pursue intended goals and avoid harmful behaviors. Perceptual alignment examines whether the way an AI communicates - its tonal, prosodic, and affective signals - accurately reflects what it knows and intends. An AI can be behaviorally aligned yet perceptually misaligned: technically correct in content while conveying false certainty, misplaced warmth, or inappropriate authority.

Tonal Contamination is the umbrella failure domain for perceptual misalignment. It describes conditions in which an AI system's vocal or communicative signals create systematically false impressions in listeners, distorting their perception of the system's reliability, emotional attunement, or epistemic state - often without the listener being aware the distortion is occurring.

The five failure modes within Tonal Contamination are:

Tonal Hallucination - projecting certainty or emotional tone not supported by the system's actual state.
Tonal Sycophancy - mirroring listener affect to generate approval rather than accurate communication.
Ambivalence Blindness - failing to detect or represent a listener's genuine uncertainty or distress.
Tonal Trust Drift - gradual erosion of listener calibration through repeated, subtle tonal inconsistency.
Authority Miscalibration - projecting inappropriate levels of expertise or deference relative to context.

Perceptual Alignment Error (PAE) is a proposed measurement signal representing the gap between the vocal signal an AI system produces and the signal that would accurately reflect its epistemic and emotional state. High PAE indicates that what the system sounds like diverges meaningfully from what it actually knows or intends - a quantifiable expression of perceptual misalignment.

The Contextual Authority Index (CAI) is a proposed measurement signal assessing whether an AI system's projected authority is appropriately calibrated to its actual competence and conversational context. A well-calibrated system adjusts its communicative authority dynamically - deferring when appropriate, asserting when warranted - rather than defaulting to a fixed tonal register regardless of context.

The white paper proposes a six-stage evaluation pipeline: (1) Baseline Tonal Profiling, (2) Contextual Stress Testing, (3) Failure Mode Tagging, (4) PAE, CCS and CAI Measurement, (5) Listener Perception Validation, and (6) Iterative Calibration. The pipeline is designed to surface perceptual misalignment before deployment and at regular audit intervals thereafter.

Perceptual alignment extends several threads from the alignment literature. Tonal Sycophancy maps onto sycophancy research in LLM behavior. Tonal Hallucination has structural parallels with deceptive alignment - arising from architectural gaps rather than intent, but producing similarly misleading outputs. The evaluation pipeline is designed to complement scalable oversight frameworks by adding a perceptual layer to capability and behavioral assessments. The research also incorporates Bayesian Teaching frameworks (Qiu et al., 2026, Nature Communications) as the reasoning layer, with perceptual alignment serving as the communication layer.

TonalityPrint is a reference dataset documenting vocal and tonal descriptors observed across 8,800+ voice interactions. It serves as a perceptual baseline for identifying and measuring tonal failure modes. It is positioned as ecological observation - not a controlled study - and is offered in the spirit of uncanny valley and perceptual trust research. TonalityPrint is publicly archived on Zenodo at DOI: 10.5281/zenodo.17913895.

The white paper is publicly archived on Zenodo and is citable via DOI: 10.5281/zenodo.19237818. For collaboration inquiries, audit engagements, or licensing discussions, please write to ronda at perceptualalignment dot com. You are also welcome to explore the broader research ecosystem at rondapolhill.com.