A Deep Dive into Existing Lip Reading Technologies

Lip reading, or Visual Speech Recognition (VSR), can revolutionize communication for those with hearing loss and in noisy environments. The researchers are trying to create advanced VSR systems using Deep learning models to overcome challenges. But still, several difficulties are there such as different shapes of lips during the video and different lightning conditions that can make deciphering or visualization difficult. Deep learning with the help of Convolutional Neural Networks (CNNs) helps to identify the exact shape and movement of a lip from a video frame. The continuous flow of lip movements in the video is handled by Recurrent Neural Networks (RNNs) which help in identifying words. However, RNNs are not good enough to remember the historical data. This issue is resolved by using Long Short Term Memory (LSTM) in the proposed research work. LSTMs have an additional internal memory that helps to store the history information for a longer period which helps to predict lip movement accurately even in rapid speech. The GRID and LRW datasets are used to generate the results. Additionally, VSR could improve video conferencing, allowing for natural communication even with compromised microphone quality. The impact of VSR extends beyond human-to-human and human-computer interaction. Frameworks like Streamlit offer a glimpse into the future, enabling the development of user-friendly lip reading applications. Deep learning unlocks the power of lip reading, paving the way for a future where communication go beyond the limitations of sound.