{"schema":"record/v1","id":"art_01M5P277P1NA7XS87AY2FHR2P5","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What speech languages and emotions was Audio2Face-3D trained on, and how does emotion control work?","summary":"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.","status":"active","body":{"text":"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.\n\nCurator 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.\n\nLimitations: 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.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","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"}]},"categories":["T07","T13"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/pdf/2508.16401","claim":"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. 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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.\n\nCurator 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.\n\nLimitations: 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.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","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"}]},"tags":[],"type":"article","links":[],"title":"What speech languages and emotions was Audio2Face-3D trained on, and how does emotion control work?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"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.","evidence":{"claims":[{"url":"https://arxiv.org/pdf/2508.16401","claim":"Problem. Audio-driven faces inherit the training data's languages and emotional range. Conditions. 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