Search Results for author:
Found 8 papers, 5 papers with code
BRAVEn: Improving Self-Supervised Pre-training for Visual and Auditory Speech Recognition
In this work, we propose BRAVEn, an extension to the recent RAVEn method, which learns speech representations entirely from raw audio-visual data.
Laughing Matters: Introducing Laughing-Face Generation using Diffusion Models
Speech-driven animation has gained significant traction in recent years, with current methods achieving near-photorealistic results.
Jointly Learning Visual and Auditory Speech Representations from Raw Data
We observe strong results in low- and high-resource labelled data settings when fine-tuning the visual and auditory encoders resulting from a single pre-training stage, in which the encoders are jointly trained.
Ranked #1 on
Speech Recognition
on LRS2
(using extra training data)
LA-VocE: Low-SNR Audio-visual Speech Enhancement using Neural Vocoders
Audio-visual speech enhancement aims to extract clean speech from a noisy environment by leveraging not only the audio itself but also the target speaker's lip movements.
SVTS: Scalable Video-to-Speech Synthesis
Video-to-speech synthesis (also known as lip-to-speech) refers to the translation of silent lip movements into the corresponding audio.
Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection
One of the most pressing challenges for the detection of face-manipulated videos is generalising to forgery methods not seen during training while remaining effective under common corruptions such as compression.
Ranked #2 on
DeepFake Detection
on FakeAVCeleb
LiRA: Learning Visual Speech Representations from Audio through Self-supervision
The large amount of audiovisual content being shared online today has drawn substantial attention to the prospect of audiovisual self-supervised learning.
End-to-End Video-To-Speech Synthesis using Generative Adversarial Networks
In this work, we propose a new end-to-end video-to-speech model based on Generative Adversarial Networks (GANs) which translates spoken video to waveform end-to-end without using any intermediate representation or separate waveform synthesis algorithm.
