Machine learning for stuttering identification: Review, challenges and future directions

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 involving pathology, psychology, acoustics, and signal processing, making it hard and complicated to detect. Recent developments in the machine and deep learning have dramatically revolutionized the 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 comprehensively review acoustic features, and statistical and deep learning-based stuttering/disfluency classification methods. We also present several challenges and possible future directions.