# What speech languages and emotions was Audio2Face-3D trained on, and how does emotion control work?

- id: art_01M5P277P1NA7XS87AY2FHR2P5
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T07, T13 · tags: none
- updated: 2026-10-24T04:20:36.154Z
- url: https://veyrul.com/articles/art_01M5P277P1NA7XS87AY2FHR2P5

Problem. Audio-driven faces inherit the training data's languages and emotional range. Conditions. Choosing a model for a non-English or highly emotional character. Method. The paper's capture used professional actors speaking English or Mandarin sentences (about 50 to 70 per actor, 3 to 15 s) across 11 emotional states. Emotion enters as a label vector that can be keyframed or blended; Audio2Emotion can detect it from audio, but its classifier uses six classes and training corpora restricted to European languages. The tongue was not captured: an animator animated it from the tracked jaw. Failure modes. Other languages may show lip shapes outside the training distribution (inference from the stated training languages); emotion labels outside the 11 states are not trained. Limitations. The paper does not report per-language error, so language bias is inferred from the data description. Version/date. arXiv 2508.16401; model v3.0.

Problem. Audio-driven faces inherit the training data's languages and emotional range. Conditions. Choosing a model for a non-English or highly emotional character. Method. The paper's capture used professional actors speaking English or Mandarin sentences (about 50 to 70 per actor, 3 to 15 s) across 11 emotional states. Emotion enters as a label vector that can be keyframed or blended; Audio2Emotion can detect it from audio, but its classifier uses six classes and training corpora restricted to European languages. The tongue was not captured: an animator animated it from the tracked jaw. Failure modes. Other languages may show lip shapes outside the training distribution (inference from the stated training languages); emotion labels outside the 11 states are not trained. Limitations. The paper does not report per-language error, so language bias is inferred from the data description. Version/date. arXiv 2508.16401; model v3.0.

Curator qualifications: Some cited evidence could not be re-located by curator GET (see mechanical_checks); no video sources. Second-tier recheck (C3R-0614a) re-located the excerpts by timed GET.

Limitations: Language bias is my inference; the authors give no per-language results. The open v3.0 weights may use different data than the paper's captures.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/pdf/2508.16401",
      "note": "Sec. 3.1 Data Capture; Sec. 3.1 Data Capture; Sec. 3.1 Tongue; Sec. 7 Results; Appendix A.3; researcher reasoning"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Problem. Audio-driven faces inherit the training data's languages and emotional range. Conditions. Choosing a model for a non-English or highly emotional character. Method. The paper's capture used professional actors speaking English or Mandarin sentences (about 50 to 70 per actor, 3 to 15 s) across 11 emotional states. Emotion enters as a label vector that can be keyframed or blended; Audio2Emotion can detect it from audio, but its classifier uses six classes and training corpora restricted to European languages. The tongue was not captured: an animator animated it from the tracked jaw. Failure modes. Other languages may show lip shapes outside the training distribution (inference from the stated training languages); emotion labels outside the 11 states are not trained. Limitations. The paper does not report per-language error, so language bias is inferred from the data description. Version/date. arXiv 2508.16401; model v3.0. · locator: Sec. 3.1 Data Capture; Sec. 3.1 Data Capture; Sec. 3.1 Tongue; Sec. 7 Results; Appendix A.3; researcher reasoning · source: https://arxiv.org/pdf/2508.16401

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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