Alhakbani Noura, Alnashwan Raghad, Al-Nafjan Abeer, Almudhi Abdulaziz
Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia.
J Clin Med. 2025 May 19;14(10):3552. doi: 10.3390/jcm14103552.
Disfluencies such as repetitions, prolongations, interjections, and blocks in sounds, syllables, or words can sometimes hinder communication. Currently, disfluencies are manually measured, which has inherent limitations, such as being time-consuming and subjective, which can lead to inconsistencies in measurement. To address these challenges, this study presents an innovative automated system for detecting disfluencies utilizing advanced artificial intelligence technologies; specifically, deep learning models such as convolutional neural networks (CNN) and convolutional long short-term memory (ConvLSTM). The system was evaluated using two benchmark datasets: FluencyBank and SEP-28K. Our proposed system demonstrates remarkable performance, achieving detection accuracies of 0.97 and 0.96, respectively, for CNNs and ConvLSTM models. These results not only exceed those of prior studies but also highlight the effectiveness of our approach in enhancing stuttering evaluation. : By providing a reliable and efficient tool for professionals in therapeutic settings, our system represents a significant advancement in the field, offering improved outcomes for individuals affected by stuttering.
诸如重复、延长、插入语以及声音、音节或单词中的停顿等言语不流畅有时会妨碍交流。目前，言语不流畅是通过人工测量的，这存在固有的局限性，比如耗时且主观，可能导致测量结果不一致。为应对这些挑战，本研究提出了一种利用先进人工智能技术检测言语不流畅的创新自动化系统；具体而言，是利用卷积神经网络（CNN）和卷积长短期记忆网络（ConvLSTM）等深度学习模型。该系统使用两个基准数据集进行了评估：FluencyBank和SEP - 28K。我们提出的系统表现出色，CNN模型和ConvLSTM模型的检测准确率分别达到了0.97和0.96。这些结果不仅超过了先前研究的结果，还突出了我们的方法在加强口吃评估方面的有效性。通过为治疗环境中的专业人员提供可靠且高效的工具，我们的系统代表了该领域的重大进步，为受口吃影响的个体带来了更好的结果。
