Towards robust voice pathology detection

Harár, Pavol; Galáž, Zoltán; Alonso-Hernández, Jesús B.; Mekyska, Jiří; Bürget, Radim; Smékal, Zdeněk

doi:10.1007/s00521-018-3464-7

Cited by 56 publications

(24 citation statements)

References 56 publications

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“…The advantage of this task in comparison with other commonly used vocal tasks is its independence of articulatory and other linguistic confounds [38]. Moreover, it is also present in most of the databases and therefore the experiments proposed in our work are comparable with other commonly used databases [39,40].…”

Section: Vocal Tasksmentioning

confidence: 65%

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

et al. 2018

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Hypokinetic dysarthria, which is associated with Parkinson's disease (PD), affects several speech dimensions, including phonation. Although the scientific community has dealt with a quantitative analysis of phonation in PD patients, a complex research revealing probable relations between phonatory features and progress of PD is missing. Therefore, the aim of this study is to explore these relations and model them mathematically to be able to estimate progress of PD during a two-year follow-up. We enrolled 51 PD patients who were assessed by three commonly used clinical scales. In addition, we quantified eight possible phonatory disorders in five vowels. To identify the relationship between baseline phonatory features and changes in clinical scores, we performed a partial correlation analysis. Finally, we trained XGBoost models to predict the changes in clinical scores during a two-year follow-up. For two years, the patients' voices became more aperiodic with increased microperturbations of frequency and amplitude. Next, the XGBoost models were able to predict changes in clinical scores with an error in range 11-26%. Although we identified some significant correlations between changes in phonatory features and clinical scores, they are less interpretable. This study suggests that it is possible to predict the progress of PD based on the acoustic analysis of phonation. Moreover, it recommends utilizing the sustained vowel /i/ instead of /a/.

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Section: Vocal Tasksmentioning

confidence: 65%

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

et al. 2018

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“…The voice samples are sustained vowel /a/ produced at normal pitch sampled with a 25 or 50 kHz sampling rate in a controlled environment. All files were downsampled to 25 kHz to have a uniform sampling frequency [2,17].…”

Section: Methodsmentioning

confidence: 99%

Optimization of Dual-Tree Complex Wavelet Packet Based Entropy Features for Voice Pathologies Detection

et al. 2020

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The Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) has been successfully implemented in numerous field because it introduces limited redundancy, provides approximately shift-invariance and geometrically oriented signal in multiple dimensions where these properties are lacking in traditional wavelet transform. This paper investigates the performance of features extracted using DT-CWPT algorithms which are quantified using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) classifiers for detecting voice pathologies. Decomposition is done on the voice signals using Shannon and Approximate entropy (ApEn) to signify the complexity of voice signals in time and frequency domain. Feature selection methods using the ReliefF algorithm and Genetic algorithm (GA) are applied to obtain the optimum features for multiclass classification. It is observed that the best accuracies obtained using DT-CWPT with ApEn entropy are 91.15 % for k-NN and 93.90 % for SVM classifiers. The proposed work provides a promising detection rate for multiple voice disorders and is useful for the development of computer-based diagnostic tools for voice pathology screening in health care facilities.

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“… In order to offer an important role in early diagnosis, progression tracking, and even the effective treatment of pathological voices, Harar et al proposed in [11] an original approach whose aim was to detect voice pathologies contained in speech signals. The authors utilized a lot of acoustic features like the raw waveforms, the spectrograms, the Melfrequency cepstral coefficients and the conventional acoustic features.…”

Section: State Of the Artmentioning

confidence: 99%

Arabic Discourse Analysis: A Naïve Algorithm for Defective Pronunciation Correction

Terbeh¹,

Zrigui²

2019

CyS

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This manuscript summarizes a novel study to detect and correct voice anomalies contained in Arabic discourses. These objectives are attained by following some fundamental steps. The first consists in classifying the Arabic produced healthy or pathological vocal signals. Second, the identification of problematic phonemes takes place. The proposition of algorithm allowing the correction of defective pronunciations presents the aim of the latter task. We are satisfied with the obtained results. Indeed, the elaborated algorithm has attained a correction performance of 90% based on 52 Arabic voice sequences covering male and female, healthy and pathological speeches and clustering several areas. Consequently, researchers of topics related to speech processing can benefit from our proposition in the conception and development of their systems.

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Towards robust voice pathology detection

Cited by 56 publications

References 56 publications

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

Optimization of Dual-Tree Complex Wavelet Packet Based Entropy Features for Voice Pathologies Detection

Arabic Discourse Analysis: A Naïve Algorithm for Defective Pronunciation Correction

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