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Introducing the VoicePrivacy Initiative
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The VoicePrivacy initiative aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges. In this paper, we formulate the voice anonymization task selected for the VoicePrivacy 2020 Challenge and describe the datasets used for system development and evaluation. We also present the attack models and the associated objective and subjective evaluation metrics. We introduce two anonymization baselines and report objective evaluation results.
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Inference Attacks for X-Vector Speaker Anonymization
A training-free inference attack that simulates the x-vector anonymization pipeline for each suspect and compares extracted x-vectors outperforms ML-based speaker identification attacks on anonymized speech.
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