68 citations · 120 across the 5 of their papers we have counts for
5 papers
End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks
Navin Raj Prabhu, Guillaume Carbajal, Nale Lehmann-Willenbrock +1
Emotions are subjective constructs. Recent end-to-end speech emotion recognition systems are typically agnostic to the subjective nature of emotions, despite their state-of-the-art…
Disentanglement Learning for Variational Autoencoders Applied to Audio-Visual Speech Enhancement
Guillaume Carbajal, Julius Richter, Timo Gerkmann
Recently, the standard variational autoencoder has been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. Vari…
Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder
Huajian Fang, Guillaume Carbajal, Stefan Wermter +1
Recently, a generative variational autoencoder (VAE) has been proposed for speech enhancement to model speech statistics. However, this approach only uses clean speech in the train…
Guided Variational Autoencoder for Speech Enhancement With a Supervised Classifier
Guillaume Carbajal, Julius Richter, Timo Gerkmann
Recently, variational autoencoders have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. However, variat…
Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise
Guillaume Carbajal, Romain Serizel, Emmanuel Vincent +1
We consider the problem of simultaneous reduction of acoustic echo, reverberation and noise. In real scenarios, these distortion sources may occur simultaneously and reducing them…