Literature DB >> 26826900

Prosodic analysis of neutral, stress-modified and rhymed speech in patients with Parkinson's disease.

Zoltan Galaz1, Jiri Mekyska1, Zdenek Mzourek1, Zdenek Smekal1, Irena Rektorova2, Ilona Eliasova3, Milena Kostalova4, Martina Mrackova3, Dagmar Berankova5.   

Abstract

BACKGROUND AND
OBJECTIVE: Hypokinetic dysarthria (HD) is a frequent speech disorder associated with idiopathic Parkinson's disease (PD). It affects all dimensions of speech production. One of the most common features of HD is dysprosody that is characterized by alterations of rhythm and speech rate, flat speech melody, and impairment of speech intensity control. Dysprosody has a detrimental impact on speech naturalness and intelligibility.
METHODS: This paper deals with quantitative prosodic analysis of neutral, stress-modified and rhymed speech in patients with PD. The analysis of prosody is based on quantification of monopitch, monoloudness, and speech rate abnormalities. Experimental dataset consists of 98 patients with PD and 51 healthy speakers. For the purpose of HD identification, sequential floating feature selection algorithm and random forests classifier is used. In this paper, we also introduce a concept of permutation test applied in the field of acoustic analysis of dysarthric speech.
RESULTS: Prosodic features obtained from stress-modified reading task provided higher classification accuracies compared to the ones extracted from reading task with neutral emotion demonstrating the importance of stress in speech prosody. Features calculated from poem recitation task outperformed both reading tasks in the case of gender-undifferentiated analysis showing that rhythmical demands can in general lead to more precise identification of HD. Additionally, some gender-related patterns of dysprosody has been observed.
CONCLUSIONS: This paper confirms reduced variation of fundamental frequency in PD patients with HD. Interestingly, increased variability of speech intensity compared to healthy speakers has been detected. Regarding speech rate disturbances, our results does not report any particular pattern. We conclude further development of prosodic features quantifying the relationship between monopitch, monoloudness and speech rate disruptions in HD can have a great potential in future PD analysis.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Dysprosody; Feature selection; Hypokinetic dysarthria; Parkinson's disease; Random forests

Mesh:

Year:  2016        PMID: 26826900     DOI: 10.1016/j.cmpb.2015.12.011

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  7 in total

Review 1.  Speech disorders in Parkinson's disease: early diagnostics and effects of medication and brain stimulation.

Authors:  L Brabenec; J Mekyska; Z Galaz; Irena Rektorova
Journal:  J Neural Transm (Vienna)       Date:  2017-01-18       Impact factor: 3.575

2.  Random Forest Algorithm Based on Speech for Early Identification of Parkinson's Disease.

Authors:  Ping Fan
Journal:  Comput Intell Neurosci       Date:  2022-05-09

3.  Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

Authors:  He-Hua Zhang; Liuyang Yang; Yuchuan Liu; Pin Wang; Jun Yin; Yongming Li; Mingguo Qiu; Xueru Zhu; Fang Yan
Journal:  Biomed Eng Online       Date:  2016-11-16       Impact factor: 2.819

4.  Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson's disease.

Authors:  Yongming Li; Xinyue Zhang; Pin Wang; Xiaoheng Zhang; Yuchuan Liu
Journal:  Neural Comput Appl       Date:  2021-02-09       Impact factor: 5.606

5.  Voice Analysis for Neurological Disorder Recognition-A Systematic Review and Perspective on Emerging Trends.

Authors:  Pascal Hecker; Nico Steckhan; Florian Eyben; Björn W Schuller; Bert Arnrich
Journal:  Front Digit Health       Date:  2022-07-07

6.  The physical significance of acoustic parameters and its clinical significance of dysarthria in Parkinson's disease.

Authors:  Shu Yang; Fengbo Wang; Liqiong Yang; Fan Xu; Man Luo; Xiaqing Chen; Xixi Feng; Xianwei Zou
Journal:  Sci Rep       Date:  2020-07-16       Impact factor: 4.379

7.  Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model for Parkinson's Disease Speech Data.

Authors:  Mingyao Yang; Jie Ma; Pin Wang; Zhiyong Huang; Yongming Li; He Liu; Zeeshan Hameed
Journal:  Diagnostics (Basel)       Date:  2021-12-09
  7 in total

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