论文标题
通过多任务和对抗性学习可靠的结巴检测
Robust Stuttering Detection via Multi-task and Adversarial Learning
论文作者
论文摘要
通过自动检测和识别口吃，言语病理学家可以跟踪口吃（PWS）的人群的发展。在本文中，我们研究了多任务（MTL）和对抗性学习（ADV）对学习强大的口吃功能的影响。这是有史以来的第一个初步研究，其中MTL和ADV已用于口吃鉴定（SI）。我们在SEP-28K口吃数据集上评估了系统，该数据集由来自385个播客的20个小时（大约）数据组成。我们的方法显示出令人鼓舞的结果，并且在各种不同类别中的基线优于基线。在基线上，我们的重复，块和插入分别提高了10％，6.78％和2％。
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.
