Villegas Bruno, Flores Kevin M, Jose Acuna Kevin, Pacheco-Barrios Kevin, Elias Dante
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:4660-4663. doi: 10.1109/EMBC.2019.8857891.
Stuttering is the principal fluency disorder that affects 1% of the world population. Growing with this disorder can impact the quality of life of the adults who stutter (AWS). To manage this condition, it is necessary to measure and assess the stuttering severity before, during and after any therapeutic process. The respiratory biosignal activity could be an option for automatic stuttering assessment, however, there is not enough evidence of its use for this purposes. Thus, the aim of this research is to develop a stuttering disfluency classification system based on respiratory biosignals. Sixty-eight participants (training: AWS=27, AWNS=33; test: AWS=9) were asked to perform a reading task while their respiratory patterns and pulse were recorded through a standardized system. Segmentation, feature extraction and Multilayer Perceptron Neural Network (MLP) was implemented to differentiate block and non-block states based on the respiratory biosignal activity. 82.6% of classification accuracy was obtained after training and testing the neural network. This work presents an accurate system to classify block and non-block states of speech from AWS during reading tasks. It is a promising system for future applications such as screening of stuttering, monitoring and biofeedback interventions.
口吃是影响全球1%人口的主要流畅性障碍。患有这种障碍会影响成年口吃者（AWS）的生活质量。为了控制这种情况，在任何治疗过程之前、期间和之后测量和评估口吃严重程度是必要的。呼吸生物信号活动可能是自动口吃评估的一种选择，然而，目前尚无足够证据表明其可用于此目的。因此，本研究的目的是开发一种基于呼吸生物信号的口吃不流畅分类系统。68名参与者（训练组：成年口吃者=27名，非口吃成年者=33名；测试组：成年口吃者=9名）被要求执行一项阅读任务，同时通过一个标准化系统记录他们的呼吸模式和脉搏。通过实施分割、特征提取和多层感知器神经网络（MLP），根据呼吸生物信号活动来区分卡顿和非卡顿状态。在对神经网络进行训练和测试后，获得了82.6%的分类准确率。这项工作提出了一个准确的系统，用于在阅读任务期间对成年口吃者的言语卡顿和非卡顿状态进行分类。它是一个有前途的系统，可用于未来的应用，如口吃筛查、监测和生物反馈干预。
