Wang Yang Yang, Gao Ke, Zhao Yunxin, Kuruvilla-Dugdale Mili, Lever Teresa E, Bunyak Filiz
Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri 65211.
Department of Speech, Language and Hearing Sciences, University of Missouri, Columbia, Missouri 65211.
IEEE EMBS Int Conf Biomed Health Inform. 2019;2019:1-4. doi: 10.1109/bhi.2019.8834506. Epub 2019 Sep 12.
Oromotor dysfunction caused by neurological disorders can result in significant speech and swallowing impairments. Current diagnostic methods to assess oromotor function are subjective and rely on perceptual judgments by clinicians. In particular, the widely used oral-diadochokinesis (oral-DDK) test, which requires rapid, alternate repetitions of speech-based syllables, is conducted and interpreted differently among clinicians. It is therefore prone to inaccuracy, which results in poor test reliability and poor clinical application. In this paper, we present a deep learning based software to extract quantitative data from the oral DDK signal, thereby transforming it into an objective diagnostic and treatment monitoring tool. The proposed software consists of two main modules: a fully automated syllable detection module and an interactive visualization and editing module that allows inspection and correction of automated syllable units. The DeepDDK software was evaluated on speech files corresponding to 9 different DDK syllables (e.g., "Pa", "Ta", "Ka"). The experimental results show robustness of both syllable detection and localization across different types of DDK speech tasks.
由神经疾病引起的口运动功能障碍可导致严重的言语和吞咽障碍。目前评估口运动功能的诊断方法具有主观性，依赖于临床医生的感知判断。特别是，广泛使用的口腔轮替运动速率（oral-DDK）测试要求快速、交替重复基于语音的音节，临床医生在进行和解释该测试时存在差异。因此，它容易出现不准确的情况，导致测试可靠性差和临床应用不佳。在本文中，我们提出了一种基于深度学习的软件，用于从口腔DDK信号中提取定量数据，从而将其转化为一种客观的诊断和治疗监测工具。所提出的软件由两个主要模块组成：一个全自动音节检测模块和一个交互式可视化与编辑模块，该模块允许对自动检测的音节单元进行检查和校正。DeepDDK软件在对应于9种不同DDK音节（如“Pa”、“Ta”、“Ka”）的语音文件上进行了评估。实验结果表明，在不同类型的DDK语音任务中，音节检测和定位都具有稳健性。
