Fri, 27 May, 07:00 - 08:00 France Time (UTC +2)
Fri, 27 May, 05:00 - 06:00 UTC
Fri, 27 May, 01:00 - 02:00 New York Time (UTC -4)
Emmanuel Vincent, Inria Nancy - Grand Est, France
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.
Emmanuel Vincent (SM'09, F'22) received the Ph.D. degree in music signal processing from Ircam in 2004 and joined Inria, the French national research institute for digital science and technology, in 2006. He is currently a Senior Research Scientist and the Head of Science of Inria Nancy - Grand Est. His research covers several speech and audio processing tasks, with a focus on privacy preservation, learning from little or no labeled data, source separation and speech enhancement, and robust speech and speaker recognition. He is a founder of the MIREX, SiSEC, CHiME, and VoicePrivacy challenge series. He is a scientific advisor of the startup company Nijta, which provides speech anonymization solutions.