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.

该论文提出了一种采用对抗式训练的序列标注模型进行不流畅语音纠正的方法，结合少量有标记的真实不流畅数据和大量的未标记数据。该技术在印度语言和英语中的不流畅语音及发音障碍引起的口吃方面均表现出色，并在 F1 得分上比竞争基线平均提高了 6.15 个百分点。这是首次利用对抗性训练进行不流畅语音更正并在英语中纠正口吃等问题，为该任务建立了新的基准。