An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

Qian, Kun; Song, Meishu; Yang, Zijiang; Ren, Zhao; Liu, Shuo; Liu, Juan; Zheng, Huaiyuan; Ji, Wei; Koike, Tomoya; Li, Xiao; Yamamoto, Yoshiharu; Schuller, Björn W.

doi:10.48550/arxiv.2005.00096

Cited by 18 publications

(23 citation statements)

References 29 publications

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“…A COVID-19-related cough detection study is presented in (15) using a cohort of 48 patients tested with COVID-19 versus other pathological coughs, in which a set of models is trained. In (11) the voice recordings of COVID-19 patients are analyzed to automatically classify the health status of the patients in four aspects, namely, the severity of the disease, the quality of sleep, fatigue and anxiety. Quatieri et al (26) showed that changes in vocal patterns could be a potential biomarker for COVID-19.…”

Section: Iatos: Proposal and Backgroundmentioning

confidence: 99%

IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples

Pizzo¹,

Esteban²

2021

Preprint

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OBJECTIVE Our objective is to evaluate the possibility of using cough audio recordings (spontaneous or simulated) to detect sound patterns in people who are diagnosed with COVID-19. The research question that led our work was: what is the sensitivity and specificity of a machine learning based COVID-19 cough classifier, using RT-PCR tests as gold standard?DESIGN Diagnostic test validation study. SETTING The audio samples that were collected for this study belong to individuals who were swabbed in the City of Buenos Aires in 20 public and 1 private facilities where RT-PCR studies were carried out on patients suspected of COVID, and 14 out-of-hospital isolation units for patients with confirmed COVID mild cases. The audios were collected through the Buenos Aires city government WhatsApp chatbot that was specifically designed to address citizen inquiries related to the coronavirus pandemic .PARTICIPANTS The data collected corresponds to 2821 individuals who were swabbed in the City of Buenos Aires, between August 11 and December 2, 2020. Individuals were divided into 1409 that tested positive for COVID-19 and 1412 that tested negative. From this sample group, 52.6% of the individuals were female and 47.4% were male. 2.5% were between the age of 0 and 20 , 61.1% between the age of 21 and 40 , 30.3% between the age of 41 and 60 and 6.1% were over 61 years of age.MAIN OUTCOME MEASURES The audio files corresponding to each individual were categorized as "positive" and "negative" based on their PCR swab test results.RESULTS Using the dataset of 2821 individuals our results showed that the neural network classifier was able to discriminate between the COVID-19 positive and the healthy coughs with an accuracy of 88%. This accuracy obtained during the training process was later tested and confirmed with a second dataset corresponding to 492 individuals.CONCLUSION Based on the reported results, we consider that the cough audio analysis could be used as a COVID-19 digital pre-entry screening tool to be used prior to conventional testing tools such as RT-PCR tests. This could help reduce wait times, ease stress, and lower exposure risk of healthcare personnel and other citizens.

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Section: Iatos: Proposal and Backgroundmentioning

confidence: 99%

IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples

Pizzo¹,

Esteban²

2021

Preprint

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“…In [17] a study of detection of coughs related to COVID-19 is presented using a cohort of 48 COVID-19 tested patients versus other pathology coughs on which an AI model is trained. In [14] speech recordings from COVID-19 patients are analyzed to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…In [16] digital stethoscope data from lung auscultation is used as a diagnostic signal for COVID-19; in [17] a study of detection of coughs related to COVID-19 collected with phones is presented using a cohort of 48 COVID-19 tested patients versus other pathological coughs on which an AI engine is trained. In [14] speech recordings from COVID-19 hospital patients are analyzed to categorize automatically the health state of patients. Our work contains an exploration of using human respiratory sounds as diagnostic markers for COVID-19 in crowdsourced, uncontrolled data.…”

Section: Introductionmentioning

confidence: 99%

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Brown,

Chauhan,

Grammenos

et al. 2020

Preprint

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Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as diagnostic or progression indicators for diseases and disease onset. However, until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC above 70% across all tasks. Clearly these results are preliminary and only scratch the surface of the possibilities of the exploitation of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.

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“…This demands for the relevant data. Very recently, a study is conducted by the authors of [27] on the speech signal of COVID-19 diagnosed patients. The behavioural parameters detected from speech includes, sleep quality, fatigue, and anxiety considering corresponding self-reported measures as ground truth and have achieved an accuracy of 0.69.…”

Section: Mental Health -Emotion Detectionmentioning

confidence: 99%

An Overview on Audio, Signal, Speech, & Language Processing for COVID-19

Deshpande,

Schuller

2020

Preprint

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Recently, there has been an increased attention towards innovating, enhancing, building, and deploying applications of speech signal processing for providing assistance and relief to human mankind from the Coronavirus (COVID-19) pandemic. Many 'AI with speech' initiatives are taken to combat with the present situation and also to create a safe and secure environment for the future. This paper summarises all these efforts taken by the research community towards helping the individuals and the society in the fight against COVID-19 over the past 3-4 months using speech signal processing. We also summarise the deep techniques used in this direction to come up with capable solutions in a short span of time. This paper further gives an overview of the contributions from non-speech modalities that may complement or serve as inspiration for audio and speech analysis. In addition, we discuss our observations with respect to solution usability, challenges, and the significant technology achievements.

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An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

Cited by 18 publications

References 29 publications

IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples

IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

An Overview on Audio, Signal, Speech, & Language Processing for COVID-19

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