LIP READING USING DEEP LEARNING TECHNIQUES

Deep Learning in layman terms is basically stacking together multiple layers of neurons. These neurons lie on connected layers where the output from the previous layer is passed or connected to every other node in the next layer. In general, the performance of neural networks is optimized by stacking several layers together. In late programmed discourse acknowledgment considerations, profound learning engineering applications for acoustic include eclipsed regular sound highlights, for example, Mel-recurrence cepstral coefficient. Be that as it may, for visual discourse acknowledgment (VSR) studies, high-quality visual element extraction systems are still broadly used. Right now, this paper aims to apply convolutional neural system (CNN) as a visual component extraction instrument for VSR. Here, three different variants of CNN is developed to process the pictures of a speaker's mouth zone in blend with phoneme marks. Later, the developed CNN variants are used to remove visual highlights basic for perceiving phonemes and are evaluated for their performance with the most widely used benchmark dataset, MIRACL-VC1.