  The goal of this work is to reconstruct speech from a silent talking face
video. Recent studies have shown impressive performance on synthesizing speech
from silent talking face videos. However, they have not explicitly considered
on varying identity characteristics of different speakers, which place a
challenge in the video-to-speech synthesis, and this becomes more critical in
unseen-speaker settings. Our approach is to separate the speech content and the
visage-style from a given silent talking face video. By guiding the model to
independently focus on modeling the two representations, we can obtain the
speech of high intelligibility from the model even when the input video of an
unseen subject is given. To this end, we introduce speech-visage selection that
separates the speech content and the speaker identity from the visual features
of the input video. The disentangled representations are jointly incorporated
to synthesize speech through visage-style based synthesizer which generates
speech by coating the visage-styles while maintaining the speech content. Thus,
the proposed framework brings the advantage of synthesizing the speech
containing the right content even with the silent talking face video of an
unseen subject. We validate the effectiveness of the proposed framework on the
GRID, TCD-TIMIT volunteer, and LRW datasets.
