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Non-negative matrix factorization-based time-frequency feature extraction of voice signal for Parkinson's disease prediction

机译：基于非负矩阵分解的时频特征提取帕金森病预测的语音信号

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摘要

Parkinson's disease (PD) is a neuron related disorder that affects the people in old age. The majority of people suffering from PD develop several voice impairments mainly related to what is known as dysarthric speech. Voice analysis can help in PD detection and in the evaluation of the dysarthria level of the patients. This study introduces time-frequency features to model discontinuities and abrupt changes that arise in the voice signal due to PD. The proposed method consists of four stages: time-frequency matrix (TFM) representation, TFM decomposition using non-negative matrix factorization (NMF), feature extraction and classification. Statistical analyses show that the proposed time-frequency features significantly differentiate between PD patients and healthy speakers. Experiments with sustained vowel phonations and isolated words of the corpus PC-GITA are conducted. The proposed method achieved average classification accuracies of up to 92% in vowels, and 97% in words. There is an improvement in accuracy ranging from 10% to 40% compared to existing methods. Further, the developed models are evaluated upon an independent dataset. Results on this separate test set show accuracies ranging from 63% to 75% in vowels, and from 53% to 75% in isolated words. Regarding the dysarthria level evaluation, Spearman's correlations between original and predicted labels are around 0.81 in sustained vowels and in isolated words. The results indicate that the proposed approach is suitable and robust for the automatic detection of PD.
机译：帕金森病（PD）是一种影响老年人的神经元相关疾病。患有PD的大多数人开发了几种与众不同的语音障碍，主要与所谓的发育性言论有关。语音分析可以帮助PD检测和评估患者的讨厌程度。本研究引入了模型不连续性和由于PD引起的语音信号中产生的突然变化的时频特征。所提出的方法由四个阶段组成：时频矩阵（TFM）表示，使用非负矩阵分解（NMF）的TFM分解，特征提取和分类。统计分析表明，所提出的时频特征显着区分PD患者和健康扬声器。进行了持续的元音音和孤立的语料PC-GITA的孤立词的实验。该方法在元音中实现了高达92％的平均分类准确性，而97％的单词。与现有方法相比，精度从10％到40％的准确性提高。此外，开发模型在独立数据集上进行评估。结果在该单独的测试套件上显示了元音的63％至75％的精度，孤立的单词的53％至75％。关于讨厌的阶级评估，原始和预测标签之间的矛盾在持续元音和孤立的单词中约为0.81。结果表明，所提出的方法适用于PD的自动检测。

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