SAIC: Integration of Speech Anonymization and Identity Classification
- URL: http://arxiv.org/abs/2312.15190v1
- Date: Sat, 23 Dec 2023 08:14:33 GMT
- Title: SAIC: Integration of Speech Anonymization and Identity Classification
- Authors: Ming Cheng, Xingjian Diao, Shitong Cheng, Wenjun Liu
- Abstract summary: 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%.
- Score: 3.8871771267431035
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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