AI Lip Reader Detecting Speech Visual Data with Deep Learning

Introducing an AI-driven lipreading system adept at decoding speech across diverse languages, including English, Tamil, and Telugu. Leveraging a deep learning architecture comprising a 3D Convolutional Neural Network (3DCNN) with Bidirectional Long Short-Term Memory (BiLSTM) units, the model achieves remarkable accuracy in transcribing spoken words solely from visual cues provided by lip movements. Addressing critical accessibility needs, this system holds promise for applications in assistive technologies and human-computer interaction systems. Through rigorous experimentation and evaluation, the lipreading model demonstrates an impressive overall accuracy of 98.4%, underscoring its efficacy and robustness in recognizing spoken words across multiple languages. Advanced evaluation techniques, including ROC curves, confusion matrices, and classification reports, provide comprehensive insights into the model’s performance, enabling targeted refinements and optimizations. This work represents a significant advancement in the field of lipreading, offering a valuable contribution to multimodal communication and fostering inclusivity in diverse linguistic contexts.