Ankitha Bekal1,,
Abhay Shetty M.,
Gourav,
Maneesh Shetty M,
- Assistant Professor, Department of Computer Science Engineering, PA College of Engineering (Affiliated to Visvesvaraya Technological University), Mangalore, Karnataka, India
- Student, Department of Computer Science Engineering, PA College of Engineering (Affiliated to Visvesvaraya Technological University), Mangalore, Karnataka, India
- Student, Department of Computer Science Engineering, PA College of Engineering (Affiliated to Visvesvaraya Technological University), Mangalore, Karnataka, India
- Student, Department of Computer Science Engineering, PA College of Engineering (Affiliated to Visvesvaraya Technological University), Mangalore, Karnataka, India
Abstract
Lip reading, the ability to interpret spoken language by observing lip movements, is a valuable skill that can aid in various applications, particularly in enhancing speech recognition systems. This project explores the implementation of a deep learning-based lip-reading model to improve the accuracy and robustness of speech recognition in challenging environments, such as noisy or audio-limited settings. The proposed lip-reading system leverages Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to effectively capture temporal and spatial features from lip movement sequences. A comprehensive data set comprising diverse speakers and linguistic contexts is used to train and evaluate the model, ensuring its generalization across different scenarios. The key steps of the project include data preprocessing, feature extraction, model training, and integration with existing speech recognition systems. The model is trained on synchronized audio-visual datasets to learn the correlation between speech signals and corresponding lip movements. To address real-world challenges, the system is designed to handle variations in lighting conditions, facial expressions, and speaker accents the performance of the lip reading-enhanced speech recognition system is evaluated through rigorous testing, comparing its accuracy and efficiency against traditional audio- only systems. The results demonstrate the potential of incorporating lip reading as a supplementary modality to improve the overall robustness and accuracy of speech recognition, especially in scenarios where audio signals alone may be insufficient. This research contributes to the growing field of multimodal deep learning and highlights the practical applications of lip reading in enhancing human-computer interaction, accessibility, and communication systems. The findings open avenues for future research in developing more advanced and context-aware models that can further bridge the gap between visual and auditory information processing in intelligent systems.
Keywords: CNN, Deep Learning, Lip-reading, speech recognition systems, RNN, facial expressions
[This article belongs to Current Trends in Signal Processing(ctsp)]
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References
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Current Trends in Signal Processing
Volume | 14 |
Issue | 01 |
Received | June 4, 2024 |
Accepted | July 4, 2024 |
Published | August 16, 2024 |