  In this study, we propose a deep neural network for reconstructing
intelligible speech from silent lip movement videos. We use auditory
spectrogram as spectral representation of speech and its corresponding sound
generation method resulting in a more natural sounding reconstructed speech.
Our proposed network consists of an autoencoder to extract bottleneck features
from the auditory spectrogram which is then used as target to our main lip
reading network comprising of CNN, LSTM and fully connected layers. Our
experiments show that the autoencoder is able to reconstruct the original
auditory spectrogram with a 98% correlation and also improves the quality of
reconstructed speech from the main lip reading network. Our model, trained
jointly on different speakers is able to extract individual speaker
characteristics and gives promising results of reconstructing intelligible
speech with superior word recognition accuracy.
