Perceptual Alignment is an emerging frontier in artificial intelligence - focused on ensuring that AI systems are not only accurate, but correctly understood by the humans who rely on them. As an independent researcher, I collaborate with organizations and institutions committed to building responsible, human-centered AI.
If your work intersects with voice technology, trust and safety, or human–AI interaction, I welcome the opportunity to collaborate with you.
Framework
Key Constructs
This research introduces a named vocabulary for perceptual failure in voice-based AI. These constructs are citable, defined, and measurable.
Tonal Contamination
Umbrella Failure Domain
The overarching category of failures in which a voice AI system's tonal or prosodic output undermines accurate human perception and trust.
Tonal Hallucination
Failure Mode
When an AI system produces tonal or prosodic signals that misrepresent the content, confidence, or intent of its output.
Tonal Sycophancy
Failure Mode
When an AI system adopts a tone calibrated to please or affirm the user rather than accurately reflect the information being conveyed.
Ambivalence Blindness
Failure Mode
When an AI system fails to tonally signal uncertainty, producing confident-sounding output in situations that warrant expressed doubt.
Tonal Trust Drift
Failure Mode
The gradual erosion or inflation of user trust caused by cumulative misalignment between an AI system's tone and its actual reliability.
Authority Miscalibration
Failure Mode
When an AI system's tonal register signals a level of authority or expertise inconsistent with its actual knowledge or role.
Perceptual Alignment Error
PAE · Measurement Signal
A proposed metric quantifying the gap between what an AI system conveys tonally and what a human user accurately perceives.
Contextual Authority Index
CAI · Measurement Signal
A proposed metric assessing whether an AI system's tonal authority register is appropriate to the context and content of its output.
Cross-Modal Congruence Score
CCS · Governing Principle & Measurement Signal
The principle that a system's text, visual, and voice outputs should jointly form a unified epistemic signal - reinforcing the same intent and confidence state across every channel and the proposed metric that estimates alignment between vocal prosody and simultaneous visual, textual, or interaction cues.
Regulatory Relevance - This research has direct relevance to emerging regulatory frameworks including the EU AI Act, particularly Article 52 transparency obligations for AI systems that interact with humans through voice. Perceptual Alignment provides an evaluable, measurable standard for assessing whether such systems meet human-centered disclosure requirements.
Engagements
Available Engagements
Perceptual Alignment Audit
A structured review of how your voice AI system handles tonality, prosody, and trust signals - evaluating for failure modes including Tonal Contamination, Tonal Sycophancy, and Authority Miscalibration.
Inquire →Voice AI Advisory Call
A focused session exploring how tonality and perception influence user trust, safety, and comprehension in your AI system - with strategic guidance for human-centered development.
Inquire →Dataset Licensing
License foundational research assets - white papers and the TonalityPrint™ Reference Dataset - to support responsible AI evaluation and development at your institution or lab.
Inquire →How We Can Work Together
Areas of Collaboration
Research Partnerships
Collaborate on studies exploring Perceptual Alignment, prosody, and human trust in AI systems.
Ideal Partners
- AI labs and research institutions
- Universities and academic consortia
- Trust and safety organizations
- Voice and conversational AI companies
Advisory
Expert insight into how tonality and perception influence user trust, safety, and comprehension in AI systems.
Services Include
- Perceptual Alignment Audits
- Voice AI Trust & Safety Assessments
- Tonality and Prosody Analysis
- Strategic Guidance for Human-Centered AI
Dataset Licensing & Evaluation
License or evaluate research assets designed to support responsible AI development.
Available Resources
- The Tonality as Attention White Paper
- The Perceptual Alignment White Paper
- The TonalityPrint™ Reference Dataset
Speaking & Thought Leadership
Presentations and briefings on Perceptual Alignment and the trust layer of human–AI interaction.
Topics Include
- Perceptual Alignment in Voice-Based AI
- Tonality as a Trust and Safety Signal
- Human-Centered Evaluation for AI Systems
- The Future of Responsible Voice Interfaces
Sponsorship & Research Support
Support independent research advancing transparency, safety, and trust in emerging AI technologies.
Opportunities
- Sponsored research initiatives
- Grants and fellowships
- Institutional partnerships
- Philanthropic contributions
Published Work
Select Research
2025
Tonality as Attention
Explores how vocal tonality influences perception, trust, and interpretation in human-AI interaction.
doi.org/10.5281/zenodo.17410581 ↗2026
TonalityPrint™ Reference Dataset
A foundational dataset supporting research into voice perception and alignment.
doi.org/10.5281/zenodo.17913895 ↗2026
Perceptual Alignment as the Next Evaluation Layer in Audio-Native AI Systems
Introduces a framework for evaluating whether AI systems are correctly understood by users - proposing Perceptual Alignment Error (PAE), Contextual Authority Index (CAI) and Cross-Modal Congruent Score (CCS) as measurable signals.
doi.org/10.5281/zenodo.19237818 ↗Collaborators
Who I Work With
I collaborate with institutions and teams working at the intersection of AI capability, safety, and human understanding.
About
About the Researcher
Ronda Polhill is an independent researcher focused on Perceptual Alignment in voice-based AI. Her work examines how tonality, prosody, and auditory perception influence trust, safety, and human understanding in artificial intelligence systems.
Her research contributes to emerging conversations in AI alignment, human-centered evaluation, and responsible technology development - including the Tonality as Attention™ Research Initiative and three peer-deposited white papers on Zenodo.
Get in Touch
Work With Me
To explore collaboration, advisory services, or research support, please reach out directly.
hello at perceptualalignment dot com- Your name and organization
- Nature of the inquiry
- Project details and timeline
- Relevant links or documentation