Vocal cord paralysis detection at earlier stage using deep learning algorithms

Abstract

The phonatory process means voicing. In the phonatory process, air newlinefrom lungs goes through glottis and gives a pressure drop over the larynx. When newlinedrop is large, vocal fold starts the oscillation. The high-speed video-endoscopy newline(HSV) is used for the study of phonatory processes, which is linked to speech. newlinePrecise identification of vocal fold boundaries at the time of vibration is used newlinefor diagnosis of speech disorder. HSV captures the image of vocal fold along newlinewith audio data, used for voice physiology and pathophysiology analysis. The newlinevocal fold is thin muscle at the back of human throat, moves to produce voice. newlineHSV is a unique laryngeal imaging technology that captures intracycle vocal newlinefold vibrations at a higher frame rate without the need of any auditory inputs. newlineHSV is effective for identification of the vibrational characteristics of the vocal newlinefolds with an increased temporal resolution during phonation. newlineClinically, vocal fold vibratory characteristics during speech is newlineretrieved through image and signal processing algorithms, extracts vocal fold newlinevibration from HSV data. Traditionally, vocal cord disorders such as laryngitis, newlinevocal nodules, vocal polyps, and vocal cord paralysis are diagnosed through newlineHSV data. Vocal cord paralysis is diagnosed through visual interpretation newlinethrough Endoscope/CT/MRI/laryngeal electromyography. However, vocal newlinecord paralysis never detect with vocal cord s muscle health. In this thesis, the newlinevocal cord paralysis i.e., vocal cords muscle health detection through vocal fold newlinecracking, stretching, tightening and shortening during vibrations. The deep newlinelearning-based diagnosis of vocal fold abnormalities are proposed in this thesis. newline