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Found 7 papers, 3 papers with code
SparseVSR: Lightweight and Noise Robust Visual Speech Recognition
We evaluate our 50% sparse model on 7 different visual noise types and achieve an overall absolute improvement of more than 2% WER compared to the dense equivalent.
SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision
Furthermore, when combined with large-scale pseudo-labeled audio-visual data SynthVSR yields a new state-of-the-art VSR WER of 16. 9% using publicly available data only, surpassing the recent state-of-the-art approaches trained with 29 times more non-public machine-transcribed video data (90, 000 hours).
Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels
Recently, the performance of automatic, visual, and audio-visual speech recognition (ASR, VSR, and AV-ASR, respectively) has been substantially improved, mainly due to the use of larger models and training sets.
Ranked #1 on
Automatic Speech Recognition (ASR)
on LRS3-TED
Audio-Visual Speech Recognition
Automatic Speech Recognition
+4
Audio-Visual Synchronisation in the wild
Finally, we set the first benchmark for general audio-visual synchronisation with over 160 diverse classes in the new VGG-Sound Sync video dataset.
Localizing Visual Sounds the Hard Way
We show that our algorithm achieves state-of-the-art performance on the popular Flickr SoundNet dataset.
VGGSound: A Large-scale Audio-Visual Dataset
Our goal is to collect a large-scale audio-visual dataset with low label noise from videos in the wild using computer vision techniques.
AutoCorrect: Deep Inductive Alignment of Noisy Geometric Annotations
We propose AutoCorrect, a method to automatically learn object-annotation alignments from a dataset with annotations affected by geometric noise.
