SAIC: Integration of Speech Anonymization and Identity Classification
This addresses de-identification challenges in healthcare applications like telehealth, though it is incremental as it integrates existing tasks without new data.
The paper tackles the problem of combining speech anonymization with identity classification by proposing SAIC, an innovative pipeline that achieves state-of-the-art performance with 96.1% top-1 accuracy on the Voxceleb1 dataset.
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.