Analysis of Acoustic Features for Speech Sound Based Classification of Asthmatic and Healthy Subjects

Yadav, Shivani; Keerthana, M; Gope, Dipanjan; Maheswari, K. Uma; Ghosh, Prasanta Kumar

doi:10.1109/icassp40776.2020.9054062

Cited by 21 publications

(18 citation statements)

References 17 publications

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“…Yadav et al. [8] used the INTERSPEECH 2013 Computational Paralinguistics Challenge (ComParE) baseline acoustic features [9] for the classification of 47 asthmatic and 48 healthy individuals with a classification accuracy of 75.4 % using voiced speech sounds. The authors compared the performance exhibited by these features with that of only MFCCs, and report an absolute improvement of 18.28 % over the accuracy given by only MFCCs.…”

Section: Introductionmentioning

confidence: 99%

AI-Based human audio processing for COVID-19: A comprehensive overview

Deshpande

Batliner

Schuller

2022

Pattern Recognition

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The Coronavirus (COVID-19) pandemic impelled several research efforts, from collecting COVID-19 patients’ data to screening them for virus detection. Some COVID-19 symptoms are related to the functioning of the respiratory system that influences speech production; this suggests research on identifying markers of COVID-19 in speech and other human generated audio signals. In this article, we give an overview of research on human audio signals using ’Artificial Intelligence’ techniques to screen, diagnose, monitor, and spread the awareness about COVID-19. This overview will be useful for developing automated systems that can help in the context of COVID-19, using non-obtrusive and easy to use bio-signals conveyed in human non-speech and speech audio productions.

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Section: Introductionmentioning

confidence: 99%

AI-Based human audio processing for COVID-19: A comprehensive overview

Deshpande

Batliner

Schuller

2022

Pattern Recognition

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“…Voice analysis can be a diagnostic method for asthma-COVID-19 detection, because both asthma and COVID-19 causes significant voice changes [1][2][3][4][5][6][7][8][9][10].…”

Section: B Asthma-covid-19 Detection Systemmentioning

confidence: 99%

“…Sound production consist of several steps: airflow from the lungs through the larynx, vocal cords vibration and resonance in the oral and nasal cavities. Lung infections such as asthma tend to impact the lung airways and significantly affect natural sound since asthma influence on lung volume and voice source is a function of lung capacity [2].…”

Section: Breath and Speech Connectionmentioning

confidence: 99%

“…The voice changes can be significant in voice analysis disease diagnosis. The research conducted [2] and [3] shows the use of voice patterns to identify pulmonary disease. In [4] and [5], acoustic voice parameter analysis was considered for laryngeal pathologies including larynx cancer.…”

Section: Breath and Speech Connectionmentioning

confidence: 99%

“…There are various studies that compare voice features and selection decisions on the most suitable ones for particular tasks. Mostly used in literature are jitter and harmonics-tonoise ratio (HNR) and jitter they used to distinguish asthma patients from health persons in [2,4,6] and they are displayed in Table 2.…”

Section: Voice Features Analysismentioning

confidence: 99%

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Voice Analysis Framework for Asthma-COVID-19 Early Diagnosis and Prediction: AI-based Mobile Cloud Computing Application

Popadina

Al-Majeed

Jalal

2021

2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

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Asthma patients come through coronavirus with higher risk, where both COVID-19 and asthma cause changes in vocal patterns, which can be detected in different ways. Monitoring systems focused on asthmatic voice quality for diagnosis are essential. However, voice monitoring has the potential to be the most accurate tool for lung early prediction of the disease. Asthma patient has a peak flow meter for daily use as an alternative for disease state monitoring, which has no special devices to detect COVID-19. This paper considers mixture methods of voice analysis for early diseases detection and their perspectives in developing for asthma and COVID-19 application, based diagnostics recognition. Mobile Cloud Computing and Artificial Intelligence used into analysis of voice parameters suitable to design an asthma oriented system for both attack prediction and COVID-19 recognition.

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A study of using cough sounds and deep neural networks for the early detection of Covid-19

Islam¹,

Abdel-Raheem²,

Tarique

2022

Biomedical Engineering Advances

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The current clinical diagnosis of COVID-19 requires person-to-person contact, needs variable time to produce results, and is expensive. It is even inaccessible to the general population in some developing countries due to insufficient healthcare facilities. Hence, a low-cost, quick, and easily accessible solution for COVID-19 diagnosis is vital. This paper presents a study that involves developing an algorithm for automated and noninvasive diagnosis of COVID-19 using cough sound samples and a deep neural network. The cough sounds provide essential information about the behavior of glottis under different respiratory pathological conditions. Hence, the characteristics of cough sounds can identify respiratory diseases like COVID-19. The proposed algorithm consists of three main steps (a) extraction of acoustic features from the cough sound samples, (b) formation of a feature vector, and (c) classification of the cough sound samples using a deep neural network. The output from the proposed system provides a COVID-19 likelihood diagnosis. In this work, we consider three acoustic feature vectors, namely (a) time-domain, (b) frequency-domain, and (c) mixed-domain (i.e., a combination of features in both time-domain and frequency-domain). The performance of the proposed algorithm is evaluated using cough sound samples collected from healthy and COVID-19 patients. The results show that the proposed algorithm automatically detects COVID-19 cough sound samples with an overall accuracy of 89.2%, 97.5%, and 93.8% using time-domain, frequency-domain, and mixed-domain feature vectors, respectively. The proposed algorithm, coupled with its high accuracy, demonstrates that it can be used for quick identification or early screening of COVID-19. We also compare our results with that of some state-of-the-art works.

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Analysis of Acoustic Features for Speech Sound Based Classification of Asthmatic and Healthy Subjects

Cited by 21 publications

References 17 publications

AI-Based human audio processing for COVID-19: A comprehensive overview

AI-Based human audio processing for COVID-19: A comprehensive overview

Voice Analysis Framework for Asthma-COVID-19 Early Diagnosis and Prediction: AI-based Mobile Cloud Computing Application

A study of using cough sounds and deep neural networks for the early detection of Covid-19

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