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

Abstract

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%.