Effect of Noise and Model Complexity on Detection of Amyotrophic Lateral Sclerosis and Parkinson’s Disease Using Pitch and MFCC

Bhattacharjee, Tanuka; Mallela, Jhansi; Atchayarcmf, Nalini; Yadav, Ravi; Reddy, P. Vijayapal; Gope, Dipanjan; Ghosh, Prasanta Kumar

doi:10.1109/icassp39728.2021.9413997

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…The study in [22] highlights the potential of fricative sounds -parameterised by duration, intensity, and/or spectral moments-to assess co-articulation capabilities and to evaluate the patient's ability to perform complex movements that are impaired in PD. This finding is also supported by [23], suggesting that fricatives are more discriminative than vowels for the detection of PD. In addition, the study in [24] explores the use of occlusive consonants for the detection of PD, achieving a classification accuracy of 94.4% with the plosive /k/, being particularly discriminative in a dataset of Spanish speakers.…”

Section: Introductionsupporting

confidence: 56%

MARTA: a model for the automatic phonemic grouping of the parkinsonian speech

Guerrero-López,

Arias-Londoño,

Shattuck-Hufnagel

et al. 2024

Preprint

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Parkinson’s disease significantly impacts speech, particularly affecting phonemic groups like stop-plosives, fricatives, and affricates. However, its objective impact on the different phonemic groups has been briefly addressed in the past. This study introduces a new model, called MARTA, built upon a Gaussian Mixture Variational AutoEncoder with metric learning to measure the disease’s impact on the phonemic grouping automatically and objectively. MARTA was trained on normophonic speech before adapting it to parkinsonian speech. The model effectively clusters phonemic groups unsupervised and demonstrates enhanced discriminative power when supervised using forced-aligned labels. Our findings reveal that beyond the traditionally affected phonemes, Parkinson’s disease not only affects stop-plosives, voiced-plosives, and nasals, but also significantly impacts liquids, vowels, and fricatives, with the model achieving a benchmarking 91% ± 9 discrimination capability. An in-depth evaluation of the impact of the disease on the different phonemic groups represents an advance in the current knowledge of its effects on the speech, and has clear implications in the speech therapy of people with Parkinson’s disease. Moreover, regardless of the specific application domain presented, the model introduced has potential downstream utility in assessing the manner of articulation, whether influenced by other medical conditions or certain dialectal variations.

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Section: Introductionsupporting

confidence: 56%

MARTA: a model for the automatic phonemic grouping of the parkinsonian speech

Guerrero-López,

Arias-Londoño,

Shattuck-Hufnagel

et al. 2024

Preprint

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A classification framework for identifying bronchitis and pneumonia in children based on a small-scale cough sounds dataset

Liao

Song²,

Wang³

et al. 2022

PLoS ONE

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Bronchitis and pneumonia are the common respiratory diseases, of which pneumonia is the leading cause of mortality in pediatric patients worldwide and impose intense pressure on health care systems. This study aims to classify bronchitis and pneumonia in children by analyzing cough sounds. We propose a Classification Framework based on Cough Sounds (CFCS) to identify bronchitis and pneumonia in children. Our dataset includes cough sounds from 173 outpatients at the West China Second University Hospital, Sichuan University, Chengdu, China. We adopt aggregation operation to obtain patients’ disease features because some cough chunks carry the disease information while others do not. In the stage of classification in our framework, we adopt Support Vector Machine (SVM) to classify the diseases due to the small scale of our dataset. Furthermore, we apply data augmentation to our dataset to enlarge the number of samples and then adopt Long Short-Term Memory Network (LSTM) to classify. After 45 random tests on RAW dataset, SVM achieves the best classification accuracy of 86.04% and standard deviation of 4.7%. The precision of bronchitis and pneumonia is 93.75% and 87.5%, and their recall is 88.24% and 93.33%. The AUC of SVM and LSTM classification models on the dataset with pitch-shifting data augmentation reach 0.92 and 0.93, respectively. Extensive experimental results show that CFCS can effectively classify children into bronchitis and pneumonia.

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Effect of Noise and Model Complexity on Detection of Amyotrophic Lateral Sclerosis and Parkinson’s Disease Using Pitch and MFCC

Cited by 2 publications

References 16 publications

MARTA: a model for the automatic phonemic grouping of the parkinsonian speech

MARTA: a model for the automatic phonemic grouping of the parkinsonian speech

A classification framework for identifying bronchitis and pneumonia in children based on a small-scale cough sounds dataset

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