Datasets:
Tasks:
Audio Classification
Modalities:
Text
Formats:
text
Sub-tasks:
audio-intent-classification
Languages:
English
Size:
< 1K
License:
| cff-version: 1.2.0 | |
| message: "If you use this dataset, please cite it as below." | |
| type: dataset | |
| title: "TonalityPrint: A Contrast-Structured Voice Dataset for Exploring Functional Tonal Intent, Ambivalence, and Inference-Time Prosodic Alignment v1.0" | |
| version: 1.0.0 | |
| doi: 10.5281/zenodo.17913895 | |
| date-released: 2026-01-24 | |
| url: "https://doi.org/10.5281/zenodo.17913895" | |
| repository-code: "https://github.com/TonalityPrint/TonalityPrint-v1" | |
| license: CC-BY-NC-4.0 | |
| authors: | |
| - family-names: Polhill | |
| given-names: Ronda | |
| email: ronda@TonalityPrint.com | |
| affiliation: Independent Researcher | |
| orcid: "" | |
| keywords: | |
| - tonality | |
| - inference | |
| - ambivalence detection | |
| - functional tonal intent | |
| - voice dataset | |
| - prosody dataset | |
| - human-AI communication | |
| - conversational AI | |
| - single speaker dataset | |
| - tonality regression | |
| - voice AI | |
| - voice alignment | |
| - fine tuning | |
| - AI safety | |
| - autonomous systems | |
| - sycophancy-mitigation | |
| - voice agents | |
| - personalized AI | |
| - embodied AI | |
| - companion AI | |
| - ethical voice data | |
| - expressive synthesis | |
| - humanoid robotics | |
| - prosodic interpretability | |
| - intent aligned dataset | |
| - inference-time prosodic alignment | |
| - trust calibration | |
| - fine-tuning dataset | |
| - human voice dataset | |
| - intent drift | |
| - tonal alignment | |
| - agentic AI | |
| - outcome inference | |
| - human-AI alignment | |
| - uncanny-valley-effect | |
| - prosodic trust | |
| - prosodic intentionality | |
| - safety alignment | |
| - prosodic style transfer | |
| - empathetic AI | |
| - humanoid voice appearance | |
| - human-in-the-loop | |
| - human baseline | |
| - real-world experience | |
| abstract: > | |
| TonalityPrint is a specialized single-speaker speech corpus designed | |
| to enable exploration of fine-tuning functional tonal intents - Trust, | |
| Attention, Reciprocity, Empathy Resonance, and Cognitive Energy - in | |
| voice AI systems. Unlike emotion recognition datasets, TonalityPrint | |
| annotates functional tonal intents (what speakers do with tone), not | |
| just what they feel. The dataset provides 144 audio samples across 18 | |
| utterances, each recorded in 8 parallel prosodic states. A core innovation | |
| is systematic ambivalence annotation, treating tonal complexity as a | |
| perceptual entropy cross-intent feature rather than noise. Utilizing a | |
| Fixed-Phrase Octet design, the dataset enables Differential Latent Analysis | |
| (DLA) for contrastive approximation of tonal intent vectors. Annotations | |
| are grounded in 8,873+ consequential interactions, capturing an AI-adjacent | |
| yet trusted vocal profile. TonalityPrint is intended as a hypothesized | |
| contrast substrate for researchers exploring inference-time alignment, | |
| prosodic interpretability, style-conditioned synthesis, human-AI voice | |
| calibration, and safety-critical voice agents. All recordings are 100% | |
| authentic human voice (author) with explicit consent, released under CC | |
| BY-NC 4.0 (academic/research free; commercial licensing available). | |
| references: | |
| - type: article | |
| title: "Tonality as Attention" | |
| authors: | |
| - family-names: Polhill | |
| given-names: Ronda | |
| year: 2025 | |
| publisher: | |
| name: Zenodo | |
| doi: 10.5281/zenodo.17410581 | |
| contact: | |
| - email: ronda@TonalityPrint.com | |
| name: Ronda Polhill | |