Lip Reading Detection Using Convolution Neural Network and Recurrent Neural Network with LSTM

Speech recognition system that has fused computer vision with deep learning to recognize spoken words with excellent accuracy. This model uses Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) units to simulate temporal relationships in the speech sequence and 3D Convolu- tional Neural Networks (3D CNN) to extract spatial and temporal characteristics from video inputs, such as lip movements. Even in challenging settings, the capabilities of combining audio-visual information allow the system to collect essential knowledge and ensure that successful recognition is warranted. TensorFlow and Keras libraries are utilized when implementing the suggested architecture, whose performance is marvelous with a 98.5% validation accuracy and 95.7% training accuracy. This technique shows how computer vision and deep learning can be combined to improve the precision and resilience of speech recognition systems.