  Understanding the lip movement and inferring the speech from it is
notoriously difficult for the common person. The task of accurate lip-reading
gets help from various cues of the speaker and its contextual or environmental
setting. Every speaker has a different accent and speaking style, which can be
inferred from their visual and speech features. This work aims to understand
the correlation/mapping between speech and the sequence of lip movement of
individual speakers in an unconstrained and large vocabulary. We model the
frame sequence as a prior to the transformer in an auto-encoder setting and
learned a joint embedding that exploits temporal properties of both audio and
video. We learn temporal synchronization using deep metric learning, which
guides the decoder to generate speech in sync with input lip movements. The
predictive posterior thus gives us the generated speech in speaker speaking
style. We have trained our model on the Grid and Lip2Wav Chemistry lecture
dataset to evaluate single speaker natural speech generation tasks from lip
movement in an unconstrained natural setting. Extensive evaluation using
various qualitative and quantitative metrics with human evaluation also shows
that our method outperforms the Lip2Wav Chemistry dataset(large vocabulary in
an unconstrained setting) by a good margin across almost all evaluation metrics
and marginally outperforms the state-of-the-art on GRID dataset.
