Voice support system using deep learning approaches for unilateral vocal cord paralyzed patients

Abstract

Vocal cord paralysis is a common problem faced by individuals, where the vocal cord fails to reverberate to produce sound waves. As a result, they are unable to speak out as they were speaking before. The proposed method is designed for aiding unilateral paralyzed peoples whose vocal cord fails to give the desired reverberations. The proposed system consists of voice-to-text and text-to-voice conversions. The voice of the paralyzed person is artificially reproduced by training a deep neural network with the unaffected voice of the patient. The confidence of the predicted output is improved by introducing voice-to-text conversion block along with the deep neural network. The performance metrics reveals the effectiveness of the proposed algorithm to reproduce natural sound. The similarity index is also high compared to that of other state-of-the-art techniques.