  By automatic detection and identification of stuttering, speech pathologists
can track the progression of disfluencies of persons who stutter (PWS). In this
paper, we investigate the impact of multi-task (MTL) and adversarial learning
(ADV) to learn robust stutter features. This is the first-ever preliminary
study where MTL and ADV have been employed in stuttering identification (SI).
We evaluate our system on the SEP-28k stuttering dataset consisting of 20 hours
(approx) of data from 385 podcasts. Our methods show promising results and
outperform the baseline in various disfluency classes. We achieve up to 10%,
6.78%, and 2% improvement in repetitions, blocks, and interjections
respectively over the baseline.
