The lip-reading project leverages deep learning to recognize speech by analyzing lip movements. This innovative application integrates video preprocessing, feature extraction, and sequence modeling techniques to achieve accurate speech recognition, aiding accessibility and human-computer interaction
Lip reading has emerged as a crucial technology for enhancing communication accessibility, especially for individuals with hearing impairments. This project aimed to build a deep learning model capable of converting silent lip movements into corresponding speech text, focusing on achieving high accuracy through advanced neural architectures.
Data Collection and Preprocessing:
Videos of speakers were processed to extract frames, focusing on the region of interest (lip movements).
Frames were normalized and augmented to improve generalization.
Feature Extraction:
A Convolutional Neural Network (CNN) was used to extract spatial features from lip movement frames.
Sequence Modeling:
A Long Short-Term Memory (LSTM) or Transformer-based model captured temporal dependencies, translating lip movements into text sequences.
Training:
The model was trained on labeled datasets using optimized loss functions and regularization to minimize overfitting.
Evaluation:
Metrics like Word Error Rate (WER) and Character Error Rate (CER) were used to assess performance.
Successfully translated complex lip movements into text with high contextual accuracy, outperforming baseline models.
Enhanced accessibility for speech-impaired users in real-time scenarios.
This project showcases the potential of deep learning in advancing lip-reading technologies. By accurately recognizing speech through lip movements, it opens avenues for improved communication tools and applications in accessibility, security, and human-computer interaction. Future work includes expanding datasets and refining real-time processing capabilities.
