3 papers
eess.SP2026
Listen to the Features: Voice Anonymization Driven by Content Embedding Matching over Signal Reconstruction
Adrien Schneider, Kacper Zabkowski, Anderson Augusma +3
The paper presents a voice anonymization model focusing on preserving content rather than producing realistic speech. It relies on content embeddings extracted from a frozen pretra…
cs.CV2026
Variational Encoder--Multi-Decoder (VE-MD) for Privacy-by-functional-design (Group) Emotion Recognition
Anderson Augusma, Dominique Vaufreydaz, Fédérique Letué
Group Emotion Recognition (GER) aims to infer collective affect in social environments such as classrooms, crowds, and public events. Many existing approaches rely on explicit indi…
cs.CV2024
Exploring VQ-VAE with Prosody Parameters for Speaker Anonymization
Sotheara Leang, Anderson Augusma, Eric Castelli +3
Human speech conveys prosody, linguistic content, and speaker identity. This article investigates a novel speaker anonymization approach using an end-to-end network based on a Vect…