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@article{174082,
        author = {SARANYA A and YAMINI G and PRIYADHARSHINI V and PRIYA V},
        title = {Bridging Speech and Sign Language with Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4726-4732},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174082},
        abstract = {This study presents an AI-driven system for real-time speech-to-sign language translation, integrating Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Convolutional Neural Networks (CNNs). A phoneme-to-gesture mapping approach enables accurate sign visualization, while deep learning techniques enhance recognition and classification. The system includes a real-time gesture synthesis module, ensuring dynamic sign representation for spoken input. An ensemble deep learning approach improves translation accuracy by optimizing phoneme recognition and gesture classification. Experimental validation using benchmark datasets demonstrates high precision in sign synthesis, making the model a promising tool for improving accessibility and communication for the hearing-impaired community.},
        keywords = {Deep Learning, Convolutional Neural Network, Natural Language Processing, Gesture Recognition, Speech-to-Sign Translation.},
        month = {April},
        }