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.

SAIC是一种集成了语音匿名化和身份分类的创新流程，通过在Voxceleb1数据集上表现出色，以96.1%的准确率达到了演讲者身份分类任务的最新水平，证明了该模型的有效性和在医疗领域推广的可能性，为未来的工作提供了有价值的指导。

SAIC: 语音匿名化与身份分类的整合