A novel pre-processing technique in pathologic voice detection: Application to Parkinson’s disease phonation

Meghraoui, Djamila; Boudraa, Bachir; Merazi-Meksen, Thouraya; Gómez-Vilda, Pedro

doi:10.1016/j.bspc.2021.102604

Cited by 19 publications

(7 citation statements)

References 26 publications

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“…Mulfari et al ( 2022 ) concentrated on word recognition using Convolutional Neural Networks (CNN), achieving an impressive 96% accuracy. Meghraoui et al ( 2021 ) employed Support Vector Machines SVM, KNN and RF achieving a 95.6% accuracy in classifying neurological characteristics. Illa et al ( 2018 ) explored supra-segmental traits, achieving 93% accuracy using SVM/DNN.…”

Section: Comparison Results Of Various Approachesmentioning

confidence: 99%

AFM signal model for dysarthric speech classification using speech biomarkers

Shabber,

Sumesh

2024

Front. Hum. Neurosci.

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Neurological disorders include various conditions affecting the brain, spinal cord, and nervous system which results in reduced performance in different organs and muscles throughout the human body. Dysarthia is a neurological disorder that significantly impairs an individual's ability to effectively communicate through speech. Individuals with dysarthria are characterized by muscle weakness that results in slow, slurred, and less intelligible speech production. An efficient identification of speech disorders at the beginning stages helps doctors suggest proper medications. The classification of dysarthric speech assumes a pivotal role as a diagnostic tool, enabling accurate differentiation between healthy speech patterns and those affected by dysarthria. Achieving a clear distinction between dysarthric speech and the speech of healthy individuals is made possible through the application of advanced machine learning techniques. In this work, we conducted feature extraction by utilizing the Amplitude and frequency modulated (AFM) signal model, resulting in the generation of a comprehensive array of unique features. A method involving Fourier-Bessel series expansion is employed to separate various components within a complex speech signal into distinct elements. Subsequently, the Discrete Energy Separation Algorithm is utilized to extract essential parameters, namely the Amplitude envelope and Instantaneous frequency, from each component within the speech signal. To ensure the robustness and applicability of our findings, we harnessed data from various sources, including TORGO, UA Speech, and Parkinson datasets. Furthermore, the classifier's performance was evaluated based on multiple measures such as the area under the curve, F1-Score, sensitivity, and accuracy, encompassing KNN, SVM, LDA, NB, and Boosted Tree. Our analyses resulted in classification accuracies ranging from 85 to 97.8% and the F1-score ranging between 0.90 and 0.97.

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Section: Comparison Results Of Various Approachesmentioning

confidence: 99%

AFM signal model for dysarthric speech classification using speech biomarkers

Shabber,

Sumesh

2024

Front. Hum. Neurosci.

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“…Vowel: 59 [ 17 , 40 , 42 , 44 , 48 , 50 , 54 – 59 , 61 – 64 , 68 , 69 , 74 – 85 , 87 – 91 , 93 – 97 , 99 , 100 , 102 , 119 – 122 , 124 , 126 – 128 , 130 – 132 , 135 , 170 , 183 , 184 , 192 ]…”

Section: Resultsunclassified

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Idrisoglu¹,

Dallora²,

Anderberg³

et al. 2023

J Med Internet Res

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Background Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. Objective This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. Methods This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. Results In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network–based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. Conclusions This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.

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“…. (15) Moving on, different frameworks, including [4,23,70] have utilized ASR principles as a steganalysis process and MOS-LQO as an assessment tool to check the integrity and/or the steganography quality-loss (SQ-Loss) between the original unprocessed speech and degraded speech version that has been passed through the steganography distorting system.…”

Section: +46607mentioning

confidence: 99%

“…ASR has significantly benefited from the latest advances made possible by deep learning (DL) algorithms, where a plethora of DL models have been proposed in the literature, offering promising performance and outperforming actual state-of-the-art techniques [15,16]. However, using DL in ASR is a challenging task that plays a crucial role in natural HMI.…”

Section: Introduction 1preliminarymentioning

confidence: 99%