Enhancing Lip Reading with Multi-Scale Video and Multi-Encoder
He Wang Pengcheng Guo Xucheng Wan Huan Zhou Lei Xie
Automatic lip-reading (ALR) aims to automatically transcribe spoken content
from a speaker's silent lip motion captured in video. Current mainstream
lip-reading approaches only use a single visual encoder to model input videos
of a single scale. In this paper, we propose to enhance lipreading by
incorporating multi-scale video data and multi-encoder. Specifically, we first
propose a novel multi-scale lip extraction algorithm based on the size of the
speaker's face and an enhanced ResNet3D visual front-end (VFE) to extract lip
features at different scales. For the multi-encoder, in addition to the
mainstream Transformer and Conformer, we also incorporate the recently proposed
Branchformer and EBranchformer as visual encoders. In the experiments, we
explore the influence of different video data scales and encoders on ALR system
performance and fuse the texts transcribed by all ALR systems using recognizer
output voting error reduction (ROVER). Finally, our proposed approach placed
second in the ICME 2024 ChatCLR Challenge Task 2, with a 21.52% reduction in
character error rate (CER) compared to the official baseline on the evaluation
set.