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The goal of this work is to reconstruct speech from a silent talking facevideo. Recent studies have shown impressive performance on synthesizing speechfrom silent talking face videos. However, they have not explicitly consideredon varying identity characteristics of different speakers, which place achallenge in the video-to-speech synthesis, and this becomes more critical inunseen-speaker settings. Our approach is to separate the speech content and thevisage-style from a given silent talking face video. By guiding the model toindependently focus on modeling the two representations, we can obtain thespeech of high intelligibility from the model even when the input video of anunseen subject is given. To this end, we introduce speech-visage selection thatseparates the speech content and the speaker identity from the visual featuresof the input video. The disentangled representations are jointly incorporatedto synthesize speech through visage-style based synthesizer which generatesspeech by coating the visage-styles while maintaining the speech content. Thus,the proposed framework brings the advantage of synthesizing the speechcontaining the right content even with the silent talking face video of anunseen subject. We validate the effectiveness of the proposed framework on theGRID, TCD-TIMIT volunteer, and LRW datasets.

VisageSynTalk: Unseen Speaker Video-to-Speech Synthesis via Speech-Visage Feature Selection