  Speech is a means of communication which relies on both audio and visual
information. The absence of one modality can often lead to confusion or
misinterpretation of information. In this paper we present an end-to-end
temporal model capable of directly synthesising audio from silent video,
without needing to transform to-and-from intermediate features. Our proposed
approach, based on GANs is capable of producing natural sounding, intelligible
speech which is synchronised with the video. The performance of our model is
evaluated on the GRID dataset for both speaker dependent and speaker
independent scenarios. To the best of our knowledge this is the first method
that maps video directly to raw audio and the first to produce intelligible
speech when tested on previously unseen speakers. We evaluate the synthesised
audio not only based on the sound quality but also on the accuracy of the
spoken words.
