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@article{197956,
        author = {PROF. JOYCE DSOUZA and JYOTI PATEL and VISHAL DHAIGUDE and CHINMAY DHOKE and SHREYASH BARI},
        title = {Real-Time Unified Communication System Integrating Sign Language, Speech, and Text Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11321-11328},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197956},
        abstract = {Communication barriers between hearing and speech-impaired individuals and normal users create significant challenges in educational institutions, workplaces, healthcare environments, and public spaces. Most existing assistive technologies focus on a single communication mode such as sign language recognition or speech-to-text conversion, limiting their effectiveness for real-time, two-way communication. This paper presents a real-time unified communication system that integrates hand gesture recognition, speech processing, text translation, text-to-speech conversion, and video communication within a single web-based platform. Hand gestures are captured using a webcam and processed using OpenCV and MediaPipe to extract 21 hand landmark points. These landmarks are supplied to a machine learning classifier developed using Scikit-learn to perform gesture-to-text conversion. Voice input is converted into text using the Google Speech-to-Text API. The generated text is translated into the required language using Google Translate and converted into speech using a text-to-speech engine. WebRTC enables real-time face-to-face communication between users. Experimental results demonstrate that the system provides accurate, fast, and reliable multimodal communication, thereby promoting inclusivity and accessibility.},
        keywords = {Sign Language Recognition, MediaPipe, Machine Learning, Multimodal Communication, Speech-to-Text, Text-to-Speech, WebRTC.},
        month = {April},
        }