Detection of voice disability and its severity in children

Abstract

Voice Disability is one of the common disabilities experienced by children. Speech being the major mode of communication, it is important to rectify the voice-related problems at an early stage in life. Painful endoscopic techniques like laryngoscopy are used by doctors to identify the voice disability. In this work, an algorithm is devised to measure the severity of voice disability in children using signal processing techniques. Spectrogram and curve fitting techniques are used to detect voice disability. The normal and pathological curve fitted functions are passed through an adaptive signal processing system. Correlation between the normal function and tuned pathological function is obtained which is used to determine the severity of the disability. The reported work on this topic is language-dependent and uses machine learning algorithms that need large databases. In this work, adaptive signal processing techniques and the use of voice acoustic parameters are explored. Sound samples used are vowel sounds that are independent of the language and a range has been assigned to quantify the severity of the disability.