A Comprehensive Approach to Sign Language Understanding: Audio-to-Gesture and Gesture-to-Audio Conversion using OpenCV
Author(s):
Sandip Vishnu Waghchaure, Deepali Deshpande, Sujit Ganesh Wagh, Vaibhav vitthal Zendage, Sameer Vinod Raut , Viraj Vijay Jethliya
Keywords:
Speech-to-Text, Voice Recognition, Keyword Extraction, NLTK (Natural Language Toolkit), Tkinter (GUI framework for Python), Audio Processing, Text Processing, Python, OpenCV, TensorFlow, pyttsx3.
Abstract
Communication is a means to express our thoughts and feelings among us. Individuals with hearing and speaking disabilities often find it challenging to communicate with those without such impairments. Numerous research efforts have been undertaken to bridge this gap, including both hardware and software solutions. Dealing with hardware products, however, can be difficult and uncomfortable. Software solutions, on the other hand, focus on enabling impaired individuals to convey their thoughts to others using various methods and technologies. The objective of our project is to establish bidirectional communication, allowing both normal and impaired individuals to interact seamlessly. Our solution comprises two main parts: Firstly, in the gesture-to-audio conversion process, the gesture is initially translated to text through data collection, application, model training, functions, and data processing. Data is collected, and then the text is converted to audio using the 'pytts3' Python library Secondly, the audio is transformed back into a gesture using speech recognition, keyword extraction (NLTK), and the resulting gesture is displayed. This project aims to bridge the communication gap between impaired and normal individuals, making the world a more inclusive and better place to live.
Article Details
Unique Paper ID: 164123

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 1086 - 1091
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