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Features extraction for the automatic detection of ALS disease from acoustic speech signals

机译:从声学语音信号自动检测ALS疾病的特征提取

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The paper presents a features for detection of pathological changes in acoustic speech signal for the diagnosis of the bulbar form of Amyotrophic Lateral Sclerosis (ALS). We collected records of the running speech test from 48 people, 26 with ALS. The proposed features are based on joint analysis of different vowels. Harmonic structure of the vowels are also taken into consideration. We also presenting the rationale of vowels selection for calculation of the proposed features. Applying this features to classification task using linear discriminant analysis (LDA) lead to overall correct classification performance of 88.0%.
机译:本文呈现了用于检测声学语音信号的病理变化的特征,用于诊断肌萎缩侧硬化(ALS)的脉冲形式的凸形形式。我们收集了从48人,26人与ALS收集的记录。所提出的特点是基于对不同元音的联合分析。也考虑了元音的谐波结构。我们还提出了用于计算拟议特征的元音选择的理由。使用线性判别分析(LDA)将此特征应用于分类任务,导致总体正确的分类性能为88.0%。

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