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Found 4 papers, 1 papers with code
A multimodal dynamical variational autoencoder for audiovisual speech representation learning
The latent space is structured to dissociate the latent dynamical factors that are shared between the modalities from those that are specific to each modality.
A vector quantized masked autoencoder for audiovisual speech emotion recognition
While fully-supervised models have been shown to be effective for audiovisual speech emotion recognition (SER), the limited availability of labeled data remains a major challenge in the field.
A vector quantized masked autoencoder for speech emotion recognition
The VQ-MAE-S model is based on a masked autoencoder (MAE) that operates in the discrete latent space of a vector-quantized variational autoencoder.
Ranked #1 on
Speech Emotion Recognition
on EmoDB Dataset
Learning and controlling the source-filter representation of speech with a variational autoencoder
Using only a few seconds of labeled speech signals generated with an artificial speech synthesizer, we propose a method to identify the latent subspaces encoding $f_0$ and the first three formant frequencies, we show that these subspaces are orthogonal, and based on this orthogonality, we develop a method to accurately and independently control the source-filter speech factors within the latent subspaces.
