SPEAK: An AI-Based Assistive Video Communication System for Speech and Sign Language Translation
Abstract
It is still very difficult for the hearing and deaf/hardof-
hearing (DHH) communities to effectively communicate, especially
when it comes to digital video conferencing. Despite
the widespread use of platforms like Zoom and Google Meet,
they frequently require costly human interpreters or invasive
hardware sensors due to their lack of native, real-time bidirectional
translation capabilities. In order to close this modality gap,
this paper presents SPEAK (Sign Processing Enhanced Audio
Kommunicator), a novel sensor-less browser-based platform. By
translating spoken language to text captions for DHH users
and sign language to text/speech for hearing users, SPEAK
enables smooth, two-way communication. By translating spoken
language to text captions for DHH users and sign language to
text/speech for hearing users, SPEAK enables smooth, two-way
communication.
For visual recognition, the system’s architecture makes use
of the Detection Transformer (DETR) model with a ResNet-50
backbone.DETR formulates detection as a direct set prediction
problem using a bipartite matching loss and self-attention mechanisms,
in contrast to conventional CNN-based detectors that
rely on region proposals. enhancing robustness against complex
backgrounds and doing away with the need for intricate, handcrafted
anchors. The audio pipeline simultaneously incorporates
Microsoft’s SpeechT5 for natural Text-to-Speech (TTS) synthesis
and OpenAI’s Whisper model for high-fidelity Automatic Speech
Recognition (ASR). optimized to save bandwidth using Voice
Activity Detection (VAD). To guarantee synchronization between
video frames and translation outputs, all modules are coordinated
within a low-latency WebRTC environment using a Flask-React
framework. SPEAK is validated as a scalable, affordable solution
for inclusive digital interaction after experimental evaluation on
a custom dataset in various lighting conditions shows a sign
detection accuracy of 92
Keywords:
Sign Language Recognition, DETR,, WebRTC,, OpenAI Whisper, Assistive Technology, Deep LearningPublished
Issue
Section
License
Copyright (c) 2026 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
- 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
- Dona S Plavelil, A Devanandha, Haritha H Kurupp, Jissin k Jose, DETECTION OF ALZHEIMER’S DISEASE AND ASSISTANCE , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Anna Thomas, Esther Thankam Mathew, Anitta Emmanuel, Noel Thomas, Auxilia: Assistive Learning Tool for Children with Down Syndrome , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Aniruddha Das, Avisikta Modak, The Carbon footprint of Machine Learning Models , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Ansamol Varghese, Anandhu Anoj, Emil Thomas, Deepta K Sunny, Angel Thomas, TrueNews: AI Powered Detection of Manipulated Text and Images , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- Parvathy V A, Irfana Parveen C A, Alisha K A, Reshma P R, Manu Krishna C P, Detection of Diabetic Retinopathy and Glaucoma using Deep Learning , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Amal Joy, Anush S Kumar, Bijal T Benny, Jismi Saju, Thushara Sukumar, PREVUE.AI: A Web-Based Intelligent Mock Interview System Using Speech and Non-Verbal Analysis , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Naveen Philip Abraham, Joppen George, Kevin Sajan, Jonathan Chandy, Jonathan Chandy, Bini M. Issac, Advancements in Assistive Technologies: Enhancing Independence and Accessibility for the Visually Impaired , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Er. Prince Abraham, AssistVoice: A Voice-Based Visual Routine Learning System for Children , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Evelyn Susan Jacob, Joel John, Raynell Rajeev, Steve Alex , Syam Gopi , Malware Classification using Image Analysis , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
You may also start an advanced similarity search for this article.
