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EER may mislead researchers about the effectiveness of voice anonymization, while privacy-ZEBRA offers a more accurate lens on information leakage.
Child-centric voice anonymization can enhance intelligibility and privacy protection, outperforming traditional adult-focused systems.
The optimal spectrogram configuration for audio and speech analysis hinges on a nuanced interplay between front-end feature representation and back-end classifier architecture, varying significantly across tasks.
Adversarial training and synthetic data can significantly boost multilingual speaker verification performance, even with limited training data.
Forget hand-crafted curricula: TSE-Datamap leverages training dynamics to automatically surface optimal learning schedules for target speaker extraction.
