Smart Communication Software for the Hearing Impaired Using Artificial Intelligence
Abstract
The development of AI-powered assistive communication software is crucial for improving interaction for individuals with speech difficulties or those who are deaf. Current Augmentative and Alternative Communication (AAC) devices often experience latency, limiting the effectiveness of real-time communication. This paper explores advanced AI technologies such as real-time speech-to-text conversion, predictive text, and auto-complete functionalities, which are designed to reduce communication delays and enhance fluency. Addition- ally, features like text-to-speech synthesis and image-based communication tools foster more seamless interactions, while environmental sound alerts and sentiment analysis provide contextual awareness. The integration of adaptive learning enables personalized experiences, allowing the software to cater to the unique needs of each user. By leveraging these innovations, the pro- posed system aims to empower individuals with communication impairments, providing them with a more intuitive and independent way to engage with their surroundings and improve their quality of life.
Keywords:
Recurrent Neural Network, Convolution Neural NetworkPublished
Issue
Section
License
Copyright (c) 2024 International Journal on Emerging Research Areas

This work is licensed under a Creative Commons Attribution 4.0 International License.
All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Similar Articles
- Denit D Binny, Diya Mathew, Jaice George, Mehak Riyas, Neenu R, A Comprehensive Survey on EMG-Based Real-Time Gesture Recognition for Prosthetic Hand Applications , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Arun Robin, Tijo Thomas Titus, Ms. Minu Cherian, Improved Handwritten Digit Recognition Using Deep Learning Technique , International Journal on Emerging Research Areas: Vol. 3 No. 2 (2023): IJERA
- Fabeela Ali Rawther, Akhil P Dominic, Alan James, Christy Chacko, Elena Maria Varghese, Early Detection of Attention Deficiency Using ML , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Emmanuel J Jose, Fidha Fathima N S, Gautham Babu, Liya Latheef, Shanthi N.M, AUDIONYX: REAL-TIME DETECTION OF AUDIO DEEPFAKES IN PHONE CALLS , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Sandra Raju, Dr S Sruthy, A Reliable Method for Detecting Brain Tumors in Magnetic Resonance Images Utilizing EfficientNet , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- B Bidhun, Deepak Dayanandan, Joel Joy, Vargheese Francis, Vani V Prakash, A Comprehensive Review of Lightweight and Attention-Driven Deep Learning Models for Automated Cataract Detection , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Anumol V S, Elna S Bijo, Neha Maria Joji, Siya Varghese, Teena George, AI-Based Medicinal Plant Identification Using Deep Learning for Mobile Applications , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Anju V Abraham, Joyal Joby, Nikhil N Nair, Saji Satheesh Kumar, Sayand K Sayand, ToothAid: A system for early detection of oral conditions , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Nihal Anil, Ms. Nighila Abhish, Jesila Joy , Noora Sajil , P R Vishnuraj, Augmented Neat Algorithm For Enhanced Cognitive Interaction (NEAT-X) , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Charukesh, Ethical Hacking using the Switch Port Analyser in a Enterprise Network , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
You may also start an advanced similarity search for this article.
