  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.
Recent work focuses solely on audio-only methods and generally aims at
inpainting music signals, which show highly different structure than speech.
Instead, we inpaint speech signals with gaps ranging from 100 ms to 1600 ms to
investigate the contribution that vision can provide for gaps of different
duration. We also experiment with a multi-task learning approach where a phone
recognition task is learned together with speech inpainting. Results show that
the performance of audio-only speech inpainting approaches degrades rapidly
when gaps get large, while the proposed audio-visual approach is able to
plausibly restore missing information. In addition, we show that multi-task
learning is effective, although the largest contribution to performance comes
from vision.
