mlsanityData to 14 Oct 2026

V.O.I.C.E (Voice, Ownership, Identity, Control, Expression): Risk Taxonomy of Synthetic Voice Generation From Empirical Data

arXiv 2604.24794 ↗First posted 25 Apr 2026cs.CR cs.AI cs.CYcited by 0

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Abstract (arXiv)

As generative voice models are rapidly advancing in both capabilities and public utilization, the unconsented collection, reuse, and synthesis of voice data are introducing new classes of privacy, security and governance risk that are poorly captured by existing, largely uniform threat models. To fill the gap, we present V.O.I.C.E, a taxonomy of voice generation risk grounded in a multi-source threat modeling effort with 569 incidents from major AI incident database, FTC and Internet Crime Complaint Center (IC3); 1067 direct incident reports from U.S. based participants across diverse groups (including voice actors, internet personalities, political personnel, and general public); and 2,221 Reddit discussions. Grounded in real-world data, our taxonomy explicitly models how risk emerges, interact with contextual factors such as degree of exposure, social visibility, and the availability of legal protections for various affected groups.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 84 references that are in the map. First posted on arXiv 25 Apr 2026.

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32 of 84 references.

References · 32

  1. 2023Neural Codec Language Models are Zero-Shot Text to Speech Synthesizerscited by 752
  2. 2017VoxCeleb: a large-scale speaker identification datasetcited by 732
  3. 2019Libri-Light: A Benchmark for ASR with Limited or No Supervisioncited by 467
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  5. 2023Voicebox: Text-Guided Multilingual Universal Speech Generation at Scalecited by 379
  6. 2018Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesiscited by 282
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  11. 2022Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reductioncited by 138
  12. 2022Does Audio Deepfake Detection Generalize?cited by 135
20 more
  1. 2026Qwen3-TTS Technical Reportcited by 118
  2. 2017Deep Voice 2: Multi-Speaker Neural Text-to-Speechcited by 75
  3. 2024Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesiscited by 48
  4. 2024Autoregressive Diffusion Transformer for Text-to-Speech Synthesiscited by 34
  5. 2023OpenVoice: Versatile Instant Voice Cloningcited by 32
  6. 2022AVQVC: One-shot Voice Conversion by Vector Quantization with applying contrastive learningcited by 24
  7. 2023Warning: Humans Cannot Reliably Detect Speech Deepfakescited by 20
  8. 2023LM-VC: Zero-shot Voice Conversion via Speech Generation based on Language Modelscited by 18
  9. 2024Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generatorscited by 18
  10. 2024Aligning AI with Public Values: Deliberation and Decision-Making for Governing Multimodal LLMs in Political Video Analysiscited by 7
  11. 2022An Overview & Analysis of Sequence-to-Sequence Emotional Voice Conversioncited by 6
  12. 2022A Hierarchical Speaker Representation Framework for One-shot Singing Voice Conversioncited by 6
  13. 2025SafeSpeech: Robust and Universal Voice Protection Against Malicious Speech Synthesiscited by 6
  14. 2024Exploring Parent-Child Perceptions on Safety in Generative AI: Concerns, Mitigation Strategies, and Design Implicationscited by 5
  15. 2024TacoLM: GaTed Attention Equipped Codec Language Model are Efficient Zero-Shot Text to Speech Synthesizerscited by 4
  16. 2024Improving Audio Codec-based Zero-Shot Text-to-Speech Synthesis with Multi-Modal Context and Large Language Modelcited by 3
  17. 2024Sound Check: Auditing Audio Datasetscited by 3
  18. 2025Human perception of audio deepfakes: the role of language and speaking stylecited by 2
  19. 2024PressProtect: Helping Journalists Navigate Social Media in the Face of Online Harassmentcited by 1
  20. 2025Why Speech Deepfake Detectors Won't Generalize: The Limits of Detection in an Open Worldcited by 1

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