169 citations · 196 across the 13 of their papers we have counts for
13 papers
Cued Speech Generation Leveraging a Pre-trained Audiovisual Text-to-Speech Model
Sanjana Sankar, Martin Lenglet, Gerard Bailly +2
This paper presents a novel approach for the automatic generation of Cued Speech (ACSG), a visual communication system used by people with hearing impairment to better elicit the s…
Is Self-Supervised Learning Enough to Fill in the Gap? A Study on Speech Inpainting
Ihab Asaad, Maxime Jacquelin, Olivier Perrotin +2
Speech inpainting consists in reconstructing corrupted or missing speech segments using surrounding context, a process that closely resembles the pretext tasks in Self-Supervised L…
Investigating the dynamics of hand and lips in French Cued Speech using attention mechanisms and CTC-based decoding
Sanjana Sankar, Denis Beautemps, Frédéric Elisei +2
Hard of hearing or profoundly deaf people make use of cued speech (CS) as a communication tool to understand spoken language. By delivering cues that are relevant to the phonetic i…
BERT, can HE predict contrastive focus? Predicting and controlling prominence in neural TTS using a language model
Brooke Stephenson, Laurent Besacier, Laurent Girin +1
Several recent studies have tested the use of transformer language model representations to infer prosodic features for text-to-speech synthesis (TTS). While these studies have exp…
Self-supervised speech unit discovery from articulatory and acoustic features using VQ-VAE
Marc-Antoine Georges, Jean-Luc Schwartz, Thomas Hueber
The human perception system is often assumed to recruit motor knowledge when processing auditory speech inputs. Using articulatory modeling and deep learning, this study examines h…
Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding
Sanjana Sankar, Denis Beautemps, Thomas Hueber
This paper proposes a simple and effective approach for automatic recognition of Cued Speech (CS), a visual communication tool that helps people with hearing impairment to understa…
Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation
Marc-Antoine Georges, Julien Diard, Laurent Girin +2
We propose a computational model of speech production combining a pre-trained neural articulatory synthesizer able to reproduce complex speech stimuli from a limited set of interpr…
A Benchmark of Dynamical Variational Autoencoders applied to Speech Spectrogram Modeling
Xiaoyu Bie, Laurent Girin, Simon Leglaive +2
The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space lea…
Learning robust speech representation with an articulatory-regularized variational autoencoder
Marc-Antoine Georges, Laurent Girin, Jean-Luc Schwartz +1
It is increasingly considered that human speech perception and production both rely on articulatory representations. In this paper, we investigate whether this type of representati…
Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input
Brooke Stephenson, Thomas Hueber, Laurent Girin +1
The prosody of a spoken word is determined by its surrounding context. In incremental text-to-speech synthesis, where the synthesizer produces an output before it has access to the…
What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS
Brooke Stephenson, Laurent Besacier, Laurent Girin +1
In incremental text to speech synthesis (iTTS), the synthesizer produces an audio output before it has access to the entire input sentence. In this paper, we study the behavior of…
Dynamical Variational Autoencoders: A Comprehensive Review
Laurent Girin, Simon Leglaive, Xiaoyu Bie +3
Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsu…
Autoencoders for music sound modeling: a comparison of linear, shallow, deep, recurrent and variational models
Fanny Roche, Thomas Hueber, Samuel Limier +1
This study investigates the use of non-linear unsupervised dimensionality reduction techniques to compress a music dataset into a low-dimensional representation which can be used i…