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.

翻译：静默是一种言语障碍,其间语言流因非自愿暂停和重复声音而中断; 静默识别是一个有趣的跨学科领域研究问题,涉及病理学、心理学、声学和信号处理,使探测工作变得困难和复杂; 机器和深层学习的最近发展使语音领域发生了巨大的革命性变化,尽管很少注意断层识别; 这项工作通过试图将跨学科领域的研究人员聚集在一起填补了空白; 本文全面审查了声学特征、统计特征和基于深层学习的静脉/混乱分类方法; 我们还提出了一些挑战和可能的未来方向。