  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.
