VE-MD uses a shared variational latent space jointly optimized for group affect classification and structural body/face decoding, delivering SOTA results on GAF-3.0 and VGAF while never producing individual emotion or identity outputs.
Exploring vq-vae with prosody pa- rameters for speaker anonymization
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Variational Encoder--Multi-Decoder (VE-MD) for Privacy-by-functional-design (Group) Emotion Recognition
VE-MD uses a shared variational latent space jointly optimized for group affect classification and structural body/face decoding, delivering SOTA results on GAF-3.0 and VGAF while never producing individual emotion or identity outputs.