International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Real-Time Speech Recognition and Indian Sign Language Translation System |
|---|---|
| ABSTRACT | Real time speech recognition and Indian sign language translation system is an innovative technology designed to bridge the communication gap between hearing and deaf or hard-of-hearing people. The proposed system is designed to transform the speech input into corresponding ISL gestures, thereby facilitating the communication process. The system is designed to integrate the speech recognition system along with Natural Language Processing techniques, especially using the Natural Language Toolkit (NLTK) library. The system is designed to first capture the speech input and transform the same into text using the speech recognition system. The text is then processed using the Natural Language Toolkit library, especially to perform tokenization, removal of stop words, stemming, and syntax analysis. The processed text is then converted into the corresponding ISL gesture, especially using the predefined ISL sign database, where the sign is represented as an animation, image, or avatar gesture. This is done to focus on linguistic issues like ambiguity, tense conversion, and grammatical restructuring to ensure proper translation into ISL. The use of NLTK also enables improvement in the pre-processing of the input text, thus increasing the efficiency of the translation process. This method will lead to the creation of intelligent human-computer interaction systems. This will also promote inclusivity, especially for the deaf community. Future improvements to the system will include the incorporation of real-time translation and the use of deep learning techniques, along with an extension of the ISL gesture set. |
| AUTHOR | Y. GAYATRI, J. SANTOSHI KUMARI, M. GANESH, M. LAXMINARASIMHA, B. SHIVA KRISHNA, K. NAVEEN KUMAR, G. CHALAPATHI Assistance Professor, Department of CSE(AI&ML), Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT), Vishakhapatnam, India Sr. Assistance Professor, Department of CSE, Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT), Vishakhapatnam, India Students, Department of CSE(AL&ML), Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT), Vishakhapatnam, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403106 |
| pdf/106_Real-Time Speech Recognition and Indian Sign Language Translation System.pdf | |
| KEYWORDS | |
| References | 1. Increasing Adaptability of a Speech into Sign Language Translation System (2018) — Discusses a Spanish speech-to-sign translation system using HMM/HSMM models along with 3D avatar animation. https://ieeexplore.ieee.org/document/8451545 2. Speech-to-Sign Language Translation System for Spanish (2019) — Presents rule-based and statistical translation approaches integrated with a 3D avatar (eSIGN project). https://ieeexplore.ieee.org/document/8859659 3. Real-Time Sign Language Recognition Framework (2021) — Describes a CNN-based system achieving high accuracy in sign language alphabet recognition. https://ieeexplore.ieee.org/document/9697289 4. An Advancement in Speech to Sign Language Translation using 3D Avatar Animator (2020) — Focuses on English-to-ISL conversion using NLP techniques and SML-based animation. https://ieeexplore.ieee.org/document/9154054 5. 3D Avatar Approach for Continuous Sign Movement Using Speech/Text (2021) — Explains the use of Blender for generating continuous and realistic sign language animations. https://ieeexplore.ieee.org/document/9422767 6. Design and Implementation of an Intelligent System to Translate Arabic Text to Arabic Sign Language (2020) — Describes a text-to-sign system using parsing techniques and 3D animation models. https://ieeexplore.ieee.org/document/9252368 7. Linguistic Modelling and Language-Processing Technologies for Avatar-Based Sign Language Presentation (2017) — Provides a linguistic framework for avatar-based sign language systems. https://www.sciencedirect.com/science/article/pii/S1877050917311843 8. A Video-Based Sign Language Translation System Using Machine Learning (2021) — Presents machine learning techniques for sign detection and translation. https://ieeexplore.ieee.org/document/9637821 9. Semantic Analyses of Text to Translate to Russian Sign Language (2016) — Discusses semantic modeling techniques for accurate text-to-sign conversion. https://link.springer.com/chapter/10.1007/978-3-319-43982- |
