AI powered visual speech recognition using deep learning

Lip reading, or visual speech recognition, is the process of interpreting spoken language by analyzing lip movements, facial expressions, and mouth patterns without relying on audio signals. This technique is particularly valuable in situations where verbal communication is hindered, such as in noisy environments or for assisting individuals with hearing impairments. In this project, deep learning-based models were developed to automate lip-reading tasks effectively. Convolutional Neural Networks (CNNs) and hybrid CNN-LSTM architectures were trained on curated visual speech datasets to recognize and predict spoken words from video sequences. The system leverages CNNs for extracting spatial features from lip regions and LSTMs for modeling temporal dynamics in speech patterns. The models were evaluated for accuracy, precision, recall, and F1-score, demonstrating promising performance in translating visual cues into textual outputs. The proposed approach highlights the potential of deep learning to advance automated lip-reading systems, offering applications in accessibility technologies, surveillance, and silent speech interfaces.