Abstract
Aphonia, a condition resulting in the loss of voice, presents significant challenges in interpersonal interactions. This project proposes a dual-pronged approach involving hand gesture recognition and voice conversion techniques to facilitate effective communication for aphonic individuals. The integration of real-time hand gesture recognition provides an alternative means of expressing ideas and emotions. By capturing and translating hand gestures into textual or auditory output, this approach offers a versatile mode of communication. Additionally, advanced voice conversion algorithms are employed to synthesize natural and intelligible speech from typed or selected text. This innovative coupling of technologies empowers aphonic individuals to engage in fluid conversations, fostering improved social interactions and enhancing their overall quality of life. A webcam is used to communicate with deaf and aphonic people. When there are modalities of communication, such as speech, that are unavailable, the human hand is the preferred option. Hand gestures that transmit concepts utilizing diverse forms and finger alignment enable human-machine interaction. The purpose of this work is to develop a hand gesture detection model and translate the results to text and audio formats. The model also responds to user voice commands and displays hand signs from the database.
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