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Found 8 papers, 1 papers with code
Speech inpainting: Context-based speech synthesis guided by video
Audio and visual modalities are inherently connected in speech signals: lip movements and facial expressions are correlated with speech sounds.
Audio-Visual Speech Inpainting with Deep Learning
In this paper, we present a deep-learning-based framework for audio-visual speech inpainting, i. e., the task of restoring the missing parts of an acoustic speech signal from reliable audio context and uncorrupted visual information.
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources.
Vocoder-Based Speech Synthesis from Silent Videos
Both acoustic and visual information influence human perception of speech.
Deep-Learning-Based Audio-Visual Speech Enhancement in Presence of Lombard Effect
Regarding speech intelligibility, we find a general tendency of the benefit in training the systems with Lombard speech.
Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems
Humans tend to change their way of speaking when they are immersed in a noisy environment, a reflex known as Lombard effect.
On Training Targets and Objective Functions for Deep-Learning-Based Audio-Visual Speech Enhancement
Audio-visual speech enhancement (AV-SE) is the task of improving speech quality and intelligibility in a noisy environment using audio and visual information from a talker.
Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification
Improving speech system performance in noisy environments remains a challenging task, and speech enhancement (SE) is one of the effective techniques to solve the problem.
