VoiceNet Taxonomy
VoiceNet
Taxonomy
57 source fields · 10 groups · 393 written anchors
A specialist vocabulary for how a voice sounds and moves through time. Read the audible quality, its local scale and the evidence boundary before assigning a value.
57
FIELDS
The verified primary inventory.
10
GROUPS
Rhythm through speaking style.
393
ANCHORS
Every supplied anchor retained.
Humaneness PerformanceNet · Shared taxonomy library
The Humaneness Script Analysis System authors performance inputs from source material. The Humaneness Performance Annotation System describes generated or recorded performance. Shared taxonomies connect prompting, annotation, representation and data selection.
Reader’s guide
Listen for the quality; use its own scale
VoiceNet is the specialist voice taxonomy within Humaneness PerformanceNet. It complements the body, interaction and multimodal fields in the PerformanceNet Taxonomy and the emotional vocabulary in the EmoNet Taxonomy.
| Inventory | Reading rule |
|---|
| 55 fields | Each has seven written anchors, 0–6. Use the actual direction and intermediate descriptions; higher is not always better, louder or more intense. |
| Background Noise | BKGN has five anchors, 0–4. Higher means less ambient noise. The source supplies no 5 or 6. |
| Content Appropriateness | EXPL has three anchors, 0–2. The audience labels require an explicit audience and use context. |
| Missing evidence | Keep null and its reason when no supported value is available. The zero anchor is a real value. |
The source introduction describes all fields as seven-level scales. Its actual tables contain the two exceptions above; this reference preserves those tables instead of inventing missing levels.
The source inventory is explicit. This pinned primary codebook supplies 57 fields. The inherited PerformanceNet master separately mentions 59 VoiceNet-Ext attributes. A verified 59-field codebook and exact 57-to-59 crosswalk are not supplied here, so this volume does not claim to contain or reconcile them.
One vocabulary across the workflow
In the Humaneness Script Analysis System, source material informs Character Makeup, scene obligations including Emotional obligations, and ordered Acting Beats with one Experience Chunk each. The resulting prompt guides an AI actor to generate audio or audio with video. In the Humaneness Performance Annotation System, an observer describes the resulting performance, or a reference recording, using available evidence.
Keep requested targets, observed labels and observer judgments in separate fields. Their shared vocabulary supports prompt construction, searchable annotation, model representations and diverse data selection. A downstream reward model needs a declared target, task and calibration; the taxonomy alone supplies neither a trained model nor a universal quality score.
Pair these fields with the PerformanceNet Situation Taxonomy when selecting scene contexts. A taxonomy coordinate does not establish that a recording exists or that the intended combination has been achieved.
What this codebook can and cannot establish
Definitions and anchors below retain the primary source wording. Some anchors describe physiology, cognition, speaker identity or idealized aesthetics. Treat these as inherited descriptions of a perceived or performed quality. A voice rating does not establish a person’s biological state, gender identity, actual age, diagnosis or private feeling. Local reading notes make this boundary explicit where needed.
Style fields can overlap. Acoustic Warmth concerns timbre rather than social affection. Resonance-placement names describe perceived sound rather than verified anatomy. Separate microphone, room and recording effects from speaker behavior whenever the evidence allows.
Use each field independently with an interval, evidence access and confidence. For learning or reward targets, declare which dimensions and values the task calls for; indiscriminately maximizing or averaging the scales would change their meaning. Neither the vocabulary nor its inherited framework attribution establishes a trained model’s accuracy.
Names, codes and abbreviations
The large title supplies the human name; the small code is the stable machine identifier. ASMR means autonomous sensory meridian response and here labels a speaking style. In the audience scale, SFS means Safe for School, SFW Safe for Work and NSFW Not Safe for Work. These source audience labels require contextual interpretation.
The structured registry retains every definition and anchor, with the unmodified primary codebook and its Apache-2.0 license. New navigation, Humaneness guidance and evidence notes are editorial additions.
Source and reuse notes
The definitions and all 393 anchors are reproduced from LAION-AI / Voice-Acting-Pipeline: VoiceNet Extended Taxonomy, pinned revision e3b0d5281558f7f73e1118c6a9b4c149bb8c0621. The primary document attributes its framework to Schumann et al. (2025) and links the VoiceNet-Ext paper; that attribution is retained here as source provenance.
The repository’s Apache License 2.0 accompanies the unmodified codebook copy. This derivative adds the Humaneness framing, navigation, readable record structure, exception inventory, machine-readable export and interpretive notes. It preserves the original definitions and anchor directions, including the short Background Noise and Content Appropriateness scales.
Primary codebook fingerprint (SHA-256): deaf836cb81bef07fd19b17d469ce2816ac7c2ce33fd7176fcbe5a09bca0bceb.