论文标题
基于持续元音发音的声学分析对ALS患者的分类
Classification of ALS patients based on acoustic analysis of sustained vowel phonations
论文作者
论文摘要
肌萎缩性侧索硬化症（ALS）是无法治愈的神经系统疾病，其进展迅速。 ALS的常见早期症状在吞咽和言语中很难。但是，言语和语音症状的早期声学表现非常可变，这使得他们的检测非常具有挑战性，无论是人类专家还是自动系统。这项研究提出了一种自动系统语音评估的方法，将健康的人与ALS患者分开。特别是，这项工作着重于分析元音 / A /和 / I /的维持发音，以对ALS患者进行自动分类。分析了各种声音特征，例如MFCC，实扣，抖动，微光，颤音，PPE，GNE，HNR等。我们还提出了一组新的声学特征，用于表征元音的谐波结构。这些特征的计算基于音调同步语音分析。线性判别分析（LDA）用于对ALS患者和健康个体患者产生的发音进行分类。测试了几种特征选择算法，以找到LDA模型的最佳特征子集。该研究的实验表明，最成功的LDA模型基于LASSO特征选择算法选择的32个功能，其精度为99.7％，灵敏度为99.3％，特异性为99.9％。在具有少量功能的分类器中，我们可以突出显示具有5个功能的LDA模型，其精度为89.0％（敏感性为87.5％和90.4％的特异性）。
Amyotrophic lateral sclerosis (ALS) is incurable neurological disorder with rapidly progressive course. Common early symptoms of ALS are difficulty in swallowing and speech. However, early acoustic manifestation of speech and voice symptoms is very variable, that making their detection very challenging, both by human specialists and automatic systems. This study presents an approach to voice assessment for automatic system that separates healthy people from patients with ALS. In particular, this work focus on analysing of sustain phonation of vowels /a/ and /i/ to perform automatic classification of ALS patients. A wide range of acoustic features such as MFCC, formants, jitter, shimmer, vibrato, PPE, GNE, HNR, etc. were analysed. We also proposed a new set of acoustic features for characterizing harmonic structure of the vowels. Calculation of these features is based on pitch synchronized voice analysis. A linear discriminant analysis (LDA) was used to classify the phonation produced by patients with ALS and those by healthy individuals. Several algorithms of feature selection were tested to find optimal feature subset for LDA model. The study's experiments show that the most successful LDA model based on 32 features picked out by LASSO feature selection algorithm attains 99.7% accuracy with 99.3% sensitivity and 99.9% specificity. Among the classifiers with a small number of features, we can highlight LDA model with 5 features, which has 89.0% accuracy (87.5% sensitivity and 90.4% specificity).
