Expert Session: EXP-10: Speech anonymization

Large-scale collection, storage, and processing of speech data poses severe privacy threats. Indeed, speech encapsulates a wealth of personal data (e.g., age and gender, ethnic origin, personality traits, health and socio-economic status, etc.) which can be linked to the speaker's identity via metadata or via automatic speaker recognition. Speech data may also be used for voice spoofing using voice cloning software. With firm backing by privacy legislations such as the European general data protection regulation (GDPR), several initiatives are emerging to develop privacy preservation solutions for speech technology. This talk focuses on voice anonymization, that is the task of concealing the speaker's voice identity without degrading the utility of the data for downstream tasks. I will i) explain how to assess privacy and utility, ii) describe the two baselines of the VoicePrivacy 2020 and 2022 Challenges and complementary methods based on adversarial learning, differential privacy, or slicing, and iii) conclude by stating open questions for future research.