  Visual speech recognition (VSR) is the task of recognizing spoken language
from video input only, without any audio. VSR has many applications as an
assistive technology, especially if it could be deployed in mobile devices and
embedded systems. The need of intensive computational resources and large
memory footprint are two of the major obstacles in developing neural network
models for VSR in a resource constrained environment. We propose a novel
end-to-end deep neural network architecture for word level VSR called MobiVSR
with a design parameter that aids in balancing the model's accuracy and
parameter count. We use depthwise-separable 3D convolution for the first time
in the domain of VSR and show how it makes our model efficient. MobiVSR
achieves an accuracy of 73\% on a challenging Lip Reading in the Wild dataset
with 6 times fewer parameters and 20 times lesser memory footprint than the
current state of the art. MobiVSR can also be compressed to 6 MB by applying
post training quantization.
