most citedThe VoicePrivacy 2022 Challenge Evaluation Plan

9 citations

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5 papers

cs.SD2022

A weighted-variance variational autoencoder model for speech enhancement

Ali Golmakani, Mostafa Sadeghi, Xavier Alameda-Pineda +1

We address speech enhancement based on variational autoencoders, which involves learning a speech prior distribution in the time-frequency (TF) domain. A zero-mean complex-valued G…

eess.SP2022

Signal Inpainting from Fourier Magnitudes

Louis Bahrman, Marina Krémé, Paul Magron +1

Signal inpainting is the task of restoring degraded or missing samples in a signal. In this paper we address signal inpainting when Fourier magnitudes are observed. We propose a ma…

cs.SD2022★ 2 cited

Are disentangled representations all you need to build speaker anonymization systems?

Pierre Champion, Denis Jouvet, Anthony Larcher

Speech signals contain a lot of sensitive information, such as the speaker's identity, which raises privacy concerns when speech data get collected. Speaker anonymization aims to t…

cs.CV2022★ 6 cited

Expression-preserving face frontalization improves visually assisted speech processing

Zhiqi Kang, Mostafa Sadeghi, Radu Horaud +1

Face frontalization consists of synthesizing a frontally-viewed face from an arbitrarily-viewed one. The main contribution of this paper is a frontalization methodology that preser…

eess.AS2022★ 9 cited

The VoicePrivacy 2022 Challenge Evaluation Plan

Natalia Tomashenko, Xin Wang, Xiaoxiao Miao +7

For new participants - Executive summary: (1) The task is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting lingu…