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

被引：0

作者：

Vashkevich, Maxim ^{[1
]}

Azarov, Elias ^{[1
]}

Petrovsky, Alexander ^{[1
]}

Rushkevich, Yuliya ^{[2
]}

机构：

[1] Belarusian State Univ Informat & Radioelect, Dept Comp Engn, 6 PBrovky str, Minsk 220013, BELARUS

[2] Republican Res & Clin Ctr Neurol & Neurosurg, Minsk, BELARUS

来源：

2018 SIGNAL PROCESSING: ALGORITHMS, ARCHITECTURES, ARRANGEMENTS, AND APPLICATIONS (SPA) | 2018年

关键词：

speech analysis; formants; ALS;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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%.

引用

页码：321 / 326

页数：6

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