Machine Learning for Stuttering Identification: Review, Challenges and Future Directions

TL;DR

This paper reviews recent machine learning approaches for stuttering identification, highlighting challenges and proposing future research directions in an interdisciplinary context involving acoustics, psychology, and signal processing.

Contribution

It provides a comprehensive review of acoustic features and classification methods, addressing the gap in applying machine learning to stuttering detection and suggesting future research avenues.

Findings

Review of acoustic features and classification techniques

Identification of key challenges in stuttering detection

Suggestions for future research directions

Abstract

Stuttering is a speech disorder during which the flow of speech is interrupted by involuntary pauses and repetition of sounds. Stuttering identification is an interesting interdisciplinary domain research problem which involves pathology, psychology, acoustics, and signal processing that makes it hard and complicated to detect. Recent developments in machine and deep learning have dramatically revolutionized speech domain, however minimal attention has been given to stuttering identification. This work fills the gap by trying to bring researchers together from interdisciplinary fields. In this paper, we review comprehensively acoustic features, statistical and deep learning based stuttering/disfluency classification methods. We also present several challenges and possible future directions.