Literature Review: Speech Disfluency Detection and Remediation Using Deep Learning Algorithms.

Speech disfluency, including stuttering, repetitions, prolongations, and involuntary pauses, significantly affects verbal communication and quality of life. Recent advances in deep learning have enabled automated techniques for speech disfluency detection using acoustic and linguistic features. However, most existing research focuses primarily on detection accuracy, with limited attention to remediation, real-time deployment, multilingual support, and computational efficiency. This paper presents a systematic review and limitation analysis of existing research works related to speech disfluency detection and remediation. Each study is analyzed based on dataset characteristics, methodology, performance metrics, and limitations. The objective of this review is to identify key research gaps and motivate the development of a comprehensive deep learning framework for real-time speech disfluency detection and remediation.