A Review of the Use of Prosodic Aspects of Speech for the Automatic Detection and Assessment of Parkinson’s Disease

Moro-Velázquez, Laureano; Dehak, Najim

doi:10.1007/978-3-030-65654-6_3

Cited by 6 publications

(4 citation statements)

References 68 publications

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“…This table also describes the expected change in the feature of people with PD. The expectation is based on experience or general knowledge of HD [35, 36, 37]. It is related to the desire to ensure sufficient clinical interpretability, and it also allows us to observe whether a feature behaves as expected in a particular dataset.…”

Section: Methodsmentioning

confidence: 99%

Exploring Digital Speech Biomarkers of Hypokinetic Dysarthria in a Multilingual Cohort

Kovac

Mekyska

Aharonson

et al. 2022

Preprint

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Motor speech disorders in patients with Parkinson’s disease (PD), collectively referred to as hypokinetic dysarthria, are the early markers of the disease. Acoustic speech features are, therefore, suitable digital biomarkers for the diagnosis and monitoring of this pathological phenomenon. At the same time, it is clear that language plays an essential role in using these features to classify speech successfully into healthy and dysarthric one. This paper focuses on the multilingual analysis of the currently used and newly proposed acoustic speech features and their use in the supportive diagnosis of PD. The goal is to explore digital speech biomarkers of PD, focusing on their language independence and high discrimination power using statistical analysis and machine learning techniques. Thirty-three acoustic features of Czech, American, Israeli, Columbian and Italian PD patients and healthy controls were analyzed using correlation and statistical tests, descriptive statistics, and the XGBoost classifier with posterior explanation by features importances and Shapley values. The features quantifying the prominence of the second formant, monopitch, and the lower number of pauses detected during text reading show the best results, both by statistical analysis and machine learning. Classification accuracies range from 67% to 85%, depending on the language. The paper introduces the concept of language robustness as a quality in which a feature behaves the same, independently of the language. By using this new concept along with other metrics, it proposes several digital speech biomarkers that have the potential to be language-independent with high discrimination power.

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Section: Methodsmentioning

confidence: 99%

Exploring Digital Speech Biomarkers of Hypokinetic Dysarthria in a Multilingual Cohort

Kovac

Mekyska

Aharonson

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This table also describes the expected change in the feature of people with PD. The expectation is based on experience or general knowledge of HD [22,21,5]. It is related to the desire to ensure sufficient clinical interpretability, and it also allows us to observe whether a feature behaves as expected in a particular dataset.…”

Section: Parametrizationmentioning

confidence: 99%

Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort

Kovac,

Mekyska,

Aharonson

et al. 2024

Biomedical Signal Processing and Control

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“…More specifically, the following speech/voice disorders could be observed: airflow insufficiency, irregular pitch fluctuations, harsh and breathy voice quality, reduced loudness, monoloudness, monopitch, unnatural speech rate, improper pausing, and imprecise articulation. For a comprehensive review on HD we refer to [25] , [26] , [27] , [28] , [29] .…”

Section: Introductionmentioning

confidence: 99%

“…The above-mentioned studies were just examples of some recently published works. For a comprehensive review, we refer to the following papers [25] , [27] , [28] .…”

Section: Introductionmentioning

confidence: 99%

Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

Skibińska,

Hosek

2023

Heliyon

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A Review of the Use of Prosodic Aspects of Speech for the Automatic Detection and Assessment of Parkinson’s Disease

Cited by 6 publications

References 68 publications

Exploring Digital Speech Biomarkers of Hypokinetic Dysarthria in a Multilingual Cohort

Exploring Digital Speech Biomarkers of Hypokinetic Dysarthria in a Multilingual Cohort

Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort

Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

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