International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Vocal Cipher

Author(s) Mr. Atharva Balasaheb Raut, Mr. Rushi Vijay Bag, Mr. Ashutosh Balwant Rode, Mr Prasad Ashok Barve
Country India
Abstract People with hearing or speech impairments frequently encounter major obstacles when communicating with others, despite the fact that communication is an essential part of human life. By translating sign language motions into matching text and voice in real time, Vocal Cypher is a cutting-edge solution created to close this communication gap. The system uses a Convolutional Neural Network (CNN) trained on sign language datasets to decode motions captured by a camera, utilizing deep learning, computer vision, and natural language processing. A text-to-speech (TTS) engine is then used to translate the identified motions into text and audible voice, facilitating smooth communication between signers and non-signers. Vocal Cypher, which is implemented using Python, TensorFlow, OpenCV, and Pyttsx3, offers an effective, precise, and user-friendly offline interface Vocal Cypher, a deep learning-based system for real-time text and audio translation of sign language motions, is presented in this work. The suggested method combines natural language processing and computer vision to enable smooth communication between non-signing and hearing-impaired people. The system uses a webcam to record and categorize motions, then uses a CNN model trained on sign language datasets to translate those gestures into understandable text and speech using a TTS engine. Vocal Cypher is an offline system that provides effective, precise, and user-friendly communication support. It is implemented using Python, TensorFlow, and OpenCV. The outcomes emphasize the model's contribution to inclusive communication technology and show how resilient it is in a variety of environmental circumstances.
The growing use of artificial intelligence in assistive technology has created new opportunities to increase accessibility for people with disabilities.
This paper presents Vocal Cypher, an intelligent system that can identify sign language motions and instantly convert them into text that can be read by humans and synthesized voice. The system uses a Convolutional Neural Network (CNN), a type of deep learning, to recognize motions in webcam-captured video frames. The translated text is then voiced using a text-to-speech engine, facilitating efficient communication between signers and non-signers. The suggested approach ensures cost-effectiveness and privacy while achieving excellent accuracy and responsiveness. Vocal Cypher is a scalable and inclusive platform that improves human-computer interaction and breaks down communication barriers for the hearing and speech-impaired community by combining AI, computer vision, and voice synthesis.
Keywords Sign Language Recognition, Deep Learning, Convolutional Neural Network (CNN), Computer Vision, Text-to-Speech (TTS), Artificial Intelligence, Gesture Recognition, Image Processing
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-15
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.60787
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