论文标题
基于语音扰动和颤音的分析的灯泡ALS检测
Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato
论文作者
论文摘要
平均而言，缺乏生物标记会导致一年的诊断延迟来检测肌萎缩性侧索硬化症（ALS）。为了改善诊断过程，可以使用基于声学分析的自动语音评估。这项工作的目的是验证维持元音发音测试的缝合性，以自动检测ALS患者。我们提出了将语音信号分隔为基本时期的增强程序，该过程需要计算扰动测量值（例如抖动和微光）。我们还提出了对维持元音发音中病理颤音表现的定量评估的方法。该研究的实验表明，使用拟议的声学分析方法，基于线性判别分析的分类器达到90.7 \％的精度，具有86.7 \％敏感性和92.2 \％特异性。
On average the lack of biological markers causes a one year diagnostic delay to detect amyotrophic lateral sclerosis (ALS). To improve the diagnostic process an automatic voice assessment based on acoustic analysis can be used. The purpose of this work was to verify the sutability of the sustain vowel phonation test for automatic detection of patients with ALS. We proposed enhanced procedure for separation of voice signal into fundamental periods that requires for calculation of perturbation measurements (such as jitter and shimmer). Also we proposed method for quantitative assessment of pathological vibrato manifestations in sustain vowel phonation. The study's experiments show that using the proposed acoustic analysis methods, the classifier based on linear discriminant analysis attains 90.7\% accuracy with 86.7\% sensitivity and 92.2\% specificity.
