Human Vocality Primitives
Whisper phonation with varying pressure, turbulence, and vowel shaping.
Structured at the articulation level using documented production workflows and secured under the Proteus Standard™.
Whisper and aspiration are airflow-driven vocal behaviors produced without sustained vocal fold vibration, relying instead on controlled breath, glottal configuration, and vocal tract shaping to generate sound. In these techniques, acoustic energy is created primarily through turbulent airflow rather than harmonic excitation, resulting in noise-shaped vocal output with no stable fundamental pitch. Expressive control emerges through subtle modulation of airflow intensity, mouth shape, articulatory configuration, and transitions into and out of silence rather than changes in loudness or pitch.
Acoustically, whisper phonation and aspiration occupy a broadband, noise-dominant timbral space characterized by diffuse spectral energy, formant-shaped filtering, and fine-grained temporal variation. Expressivity is conveyed through airflow texture, articulatory gesture timing, and the interaction between breath noise and the resonant properties of the vocal tract. These characteristics make whisper and aspiration particularly well suited for modeling unvoiced vocal behavior, breath-aware speech systems, and airflow-centric sound analysis, where precise control of onset behavior, noise structure, and repeatable articulatory gestures is essential.
Key technical details for this dataset — including file counts, duration, delivery format, and session context.
Planned technical specifications and recording standards for this dataset.
Total Files:
126
Total Files (Preview):
Total Duration (Hours):
0.21
Sample Rate (Hz):
96000
Bit Depth (Delivery):
24
Dataset Version:
v1.0
Recording Environment:
Treated Studio
Microphone Configuration:
Rode NT1-A positioned 4-5 inches from mouth
Performer:
Blake Pullen
Recording Dates:
Jan. 2nd 2026
Recording Location:
Las Vegas, NV
Produced using standardized capture, editing, and QC protocols with versioned metadata and Proteus-backed provenance.
An overview of what’s included in this dataset — from articulations and performance styles to session context and recording notes.
This dataset contains a comprehensive collection of recordings capturing whisper phonation and aspiration-based vocal gestures, recorded in isolation to emphasize airflow-driven, unvoiced vocal behavior. The material is designed to document the structural organization, capture quality, and articulatory taxonomy of whisper and aspiration as foundational vocal primitives, rather than convey semantic speech content or linguistic meaning.
Included recordings emphasize sustained whisper phonation, fricative-based airflow gestures, variations in mouth shape and articulatory configuration, and controlled transitions into and out of silence. All material is presented in a neutral, non-performative context to support expressive voice modeling, speech research, and airflow-centric audio analysis workflows. Together, the dataset provides a structured, repeatable corpus suitable for studying and modeling unvoiced vocal behavior across a range of airflow and articulatory conditions.
All audio was recorded in a controlled studio environment using standardized capture, editing, and QC protocols applied consistently across the Harmonic Frontier Audio catalog.
Source material was captured at 32-bit float to preserve full dynamic headroom and minimize quantization artifacts during editing and processing. Final dataset files are delivered as 24-bit PCM for consistency and downstream compatibility.
A single performer and vocal source were used consistently across all sessions to maintain physiological continuity, articulatory stability, and repeatable airflow behavior.
Vocal source details:
Human voice — whisper phonation and aspiration techniques
Additional processing was limited to trimming, fade handling, and integrity checks. No creative processing, normalization, compression, or dynamic shaping was applied beyond what was necessary for clean, faithful delivery of the recorded material.
A structured breakdown of the expressive building blocks in this dataset — including articulations, dynamics, transitions, and any extended techniques captured during recording.
Unlike clip- or phrase-based datasets, this dataset is structured at the articulation and gesture level. This enables interpretable control, expressive variability, and human-aligned modeling, but significantly increases production complexity and significantly limits who can produce such datasets correctly at scale.
This dataset includes a structured set of whisper and aspiration-based vocal articulations, recorded in isolation to support articulation-aware modeling and analysis of unvoiced vocal behavior.
Articulations include:
• Sustained whisper phonation at varying airflow intensities
• Fricative-based whisper gestures produced through controlled articulatory shaping
• Mouth-shape and constriction variations affecting airflow texture
• Controlled onsets, offsets, and transitions into and out of silence
• Aspiration-dominant airflow events without sustained phonation
Articulations are recorded without linguistic content, accompaniment, or rhythmic framing to preserve clarity, separability, and modeling utility across speech, voice synthesis, and airflow-centric analysis contexts.
This dataset includes a focused set of gesture-level vocal behaviors capturing extended whisper and aspiration techniques, with an emphasis on airflow control, articulatory shaping, and transitions at the threshold between phonation and silence.
Gesture types include:
• Micro-variation in airflow intensity and breath stability during sustained whisper phonation
• Articulatory constriction and release shaping fricative-based whisper gestures
• Subtle changes in mouth configuration affecting spectral texture and noise distribution
• Controlled transitions into and out of silence, including aspiration-dominant edge cases
• Near-threshold vocal behaviors where airflow, noise, and room tone converge
Gestures are recorded in isolation and without linguistic framing to preserve clarity, repeatability, and modeling utility, supporting detailed analysis of unvoiced vocal behavior and breath-driven sound generation across expressive voice and speech research contexts.
This dataset was recorded, documented, and released under The Proteus Standard™, Harmonic Frontier Audio’s framework for rights-cleared, provenance-audited audio data.
The Proteus Standard ensures:
•Performer-owned, contract-clean source material
•Transparent recording methodology and metadata
•Consistent capture, QC, and documentation practices across the catalog
Learn more about The Proteus Standard
Captured with expert musicians and vocalists across global traditions — ensuring each dataset carries authentic nuance, human expression, and rights-managed provenance.
Blake Pullen is a multidisciplinary vocalist, musician, and recording engineer with a background spanning formal vocal performance, traditional acoustic music, and high-fidelity audio production.
With formal training in vocal performance and extensive experience recording both instruments and extended vocal techniques, Blake approaches dataset creation from a physiological, acoustic, and systems-oriented perspective. His work emphasizes articulatory precision, repeatability across sessions, and capture practices designed to support long-term machine learning, speech research, and expressive voice modeling rather than performative presentation.
As the founder of Harmonic Frontier Audio, he performs and records the initial datasets to establish a consistent technical and methodological foundation for the catalog, ensuring that vocal capture techniques, articulation taxonomy, and provenance standards are applied rigorously from the outset.
A three-part listening benchmark: a mixed musical demo built from this dataset, the raw source clip, and an AI model’s attempt to reproduce the same prompt.
A musical demonstration created by replacing a state-of-the-art AI-generated lead instrument with original source recordings from this dataset, then arranged and mastered to preserve musical context. This approach allows direct comparison between current-generation model output and real, rights-cleared acoustic source material.
Directly from the dataset: an isolated, unprocessed example of the source recording.
An unmodified output from a current-gen AI model given the same musical prompt. Included to illustrate where today’s systems still differ from real, recorded sources.
AI model approximations generated using publicly available state-of-the-art music generation systems.
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