mlsanityData to 14 Oct 2026

SAIC: Integration of Speech Anonymization and Identity Classification

arXiv 2312.15190 ↗First posted 23 Dec 2023cs.SD cs.AI cs.CRcited by 7

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Abstract (arXiv)

Speech anonymization and de-identification have garnered significant attention recently, especially in the healthcare area including telehealth consultations, patient voiceprint matching, and patient real-time monitoring. Speaker identity classification tasks, which involve recognizing specific speakers from audio to learn identity features, are crucial for de-identification. Since rare studies have effectively combined speech anonymization with identity classification, we propose SAIC - an innovative pipeline for integrating Speech Anonymization and Identity Classification. SAIC demonstrates remarkable performance and reaches state-of-the-art in the speaker identity classification task on the Voxceleb1 dataset, with a top-1 accuracy of 96.1%. Although SAIC is not trained or evaluated specifically on clinical data, the result strongly proves the model's effectiveness and the possibility to generalize into the healthcare area, providing insightful guidance for future work.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 27 of its 36 references that are in the map. First posted on arXiv 23 Dec 2023.

Builds on

27 of 36 references.

References · 27

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15 more
  1. 2019One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalizationcited by 82
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  15. 2020Speech Pseudonymisation Assessment Using Voice Similarity Matricescited by 8

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Citing papers per year: 2024: 6; 2025: 1.

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