Paul-Gauthier Noé — accepted AI-conference papers

2023

Hiding Speaker's Sex in Speech Using Zero-Evidence Speaker Representation in an Analysis/Synthesis Pipeline

Paul-Gauthier Noé, Xiaoxiao Miao, Xin Wang, Junichi Yamagishi, Jean-François Bonastre, Driss Matrouf

ICASSP 2023accepted

The use of modern vocoders in an analysis/synthesis pipeline allows us to investigate high-quality voice conversion that can be used for privacy purposes. Here, we propose to transform the speaker embedding and the pitch in order to hide the sex of the speaker. ECAPA-TDNN-based speaker representatio…

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2022

A Bridge between Features and Evidence for Binary Attribute-Driven Perfect Privacy

Paul-Gauthier Noé, Andreas Nautsch, Driss Matrouf, Pierre-Michel Bousquet, Jean-François Bonastre

ICASSP 2022accepted

Attribute-driven privacy aims to conceal a single user’s attribute, contrary to anonymisation that tries to hide the full identity of the user in some data. When the attribute to protect from malicious inferences is binary, perfect privacy requires the log-likelihood-ratio to be zero resulting in no…

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2020

CGCNN: Complex Gabor Convolutional Neural Network on Raw Speech

Paul-Gauthier Noé, Titouan Parcollet, Mohamed Morchid

ICASSP 2020accepted

Convolutional Neural Networks (CNN) have been used in Automatic Speech Recognition (ASR) to learn representations directly from the raw signal instead of hand-crafted acoustic features, providing a richer and lossless input signal. Recent researches propose to inject prior acoustic knowledge to the…

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