Automatic Speech and Voice Disorder Detection Using Deep Learning—A Systematic Literature Review

Abstract

Speech and voice disorders are highly prevalent and a significant concern, particularly among children. These disorders have a notable impact on individual's personalities, academic performance and overall development. Early detection and intervention of these disorders surely help out and generate an effective outcome. Automatic detection of speech and voice disorders at an early stage plays a crucial role in identifying and addressing these communication challenges because of their efficiency over traditional diagnostic methods. Over time, numerous techniques were developed in the past, but some of them have become less efficient in the present scenario. Deep Learning has surfaced as a recent and significant advancement in detecting speech and voice disorders. This paper provides a systematic review of the utilization of deep learning techniques for the detection of speech and voice disorders. The review encompasses studies published from 2018 to 2023 exploring various architectures of deep learning models for capturing complex patterns in speech data. Each deep learning based speech sound disorder detection technique is discussed with critical appraisal and relevant benchmarks available for evaluation of results. This review holds significance for new researchers who are interested in exploring the field of automatic speech disorder detection as the paper concludes by discussing future directions and potential areas of improvement in automatic speech sound disorder detection using deep learning.