  Disfluencies commonly occur in conversational speech. Speech with
disfluencies can result in noisy Automatic Speech Recognition (ASR)
transcripts, which affects downstream tasks like machine translation. In this
paper, we propose an adversarially-trained sequence-tagging model for
Disfluency Correction (DC) that utilizes a small amount of labeled real
disfluent data in conjunction with a large amount of unlabeled data. We show
the benefit of our proposed technique, which crucially depends on synthetically
generated disfluent data, by evaluating it for DC in three Indian languages-
Bengali, Hindi, and Marathi (all from the Indo-Aryan family). Our technique
also performs well in removing stuttering disfluencies in ASR transcripts
introduced by speech impairments. We achieve an average 6.15 points improvement
in F1-score over competitive baselines across all three languages mentioned. To
the best of our knowledge, we are the first to utilize adversarial training for
DC and use it to correct stuttering disfluencies in English, establishing a new
benchmark for this task.
