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The Deep Weighted Features play a significant role in extraction of visual features, thus, we present an algorithm to analyze and understand the language of a person with the help of lips movements. The proposed weighted visual features extraction of deep learning makes the system analyze and understand the words used by the person. The weights are calculated from the movement of lips by assigning labels to the shapes of lips in each and every frame of a video with reference to time. The sequence of frames of a video is processed for calculating the weights from the shape of a lip with respect to time in sequence. Thereby we shall train the system to analyze, which character or word is spoken by the person. The deep weighted feature descriptors help the system to analyze and understand the word corresponding to the shape of lips processed in sequence with respect to time. The weighted feature descriptor of deep learning has yielded an accuracy of 84.82% on a dataset provided by the deaf and dumb institution. Further, the methodology works on reading the language of Kannada by analyzing the movement of lips.