Acoustic Characteristics of Voice and Speech in Post-COVID-19

Background/Objectives: The aim of this paper was to compare voice and speech characteristics between post-COVID-19 and control subjects. The hypothesis was that acoustic parameters of voice and speech may differentiate subjects infected by COVID-19 from control subjects. Additionally, we expected to...

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Published in:	Healthcare (Basel) Vol. 13; no. 1; p. 63
Main Authors:	Berti, Larissa Cristina, Gauy, Marcelo, da Silva, Luana Cristina Santos, Rios, Julia Vasquez Valenci, Morais, Viviam Batista, Almeida, Tatiane Cristina de, Sossolete, Leisi Silva, Quirino, José Henrique de Moura, Martins, Carolina Fernanda Pentean, Fernandes-Svartman, Flaviane R., Raposo de Medeiros, Beatriz, Queiroz, Marcelo, Gazzola, Murilo, Finger, Marcelo
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.01.2025 MDPI
Subjects:	Acoustics Age groups Antigens Artificial intelligence Biomarkers Brain research COVID-19 Cytokines Fatigue Health aspects Hospitalization Infections Long COVID Respiratory failure Speaking Speech Voice Brazil voice phonetics speech acoustics adult post-acute COVID-19 syndrome
ISSN:	2227-9032, 2227-9032
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Abstract	Background/Objectives: The aim of this paper was to compare voice and speech characteristics between post-COVID-19 and control subjects. The hypothesis was that acoustic parameters of voice and speech may differentiate subjects infected by COVID-19 from control subjects. Additionally, we expected to observe the persistence of symptoms in women. Methods: In total, 134 subjects participated in the study, were selected for convenience and divided into two groups: 70 control subjects and 64 post-COVID-19 subjects, with an average time of 8.7 months after infection. The recordings were made using the SPIRA software (v.1.0.) on cell phones, based on three verbal tasks: sustained production of the vowel/a/, reading a sentence, and producing a rhyme. Acoustic analyses of speech and voice were carried out with the PRAAT software (v.4.3.18), based on the following parameters: total sentence duration, number of pauses, pause duration, f0, f0SD, jitter, shimmer, and harmonics-to-noise ratio (HNR). Results: Regarding the acoustic characteristics of speech, there were no differences between the groups or between the sexes. Regarding the acoustic characteristics of voice, jitter, shimmer, and HNR, significant differences between the groups were found. Differences between sexes were observed in the following frequency-related parameters: f0, f0SD, and jitter. Conclusions: Some acoustic characteristics of the patients’ voice may show a deteriorated condition even after exacerbation of the disease. These characteristics are compatible with some of the symptoms reported by post-COVID-19 subjects, such as the presence of tension and fatigue. These voice acoustic parameters could be used as biomarkers to screen voice disorders in long-COVID, using artificial intelligence (AI), accelerating the search for diagnosis by specialists.
AbstractList	Background/Objectives: The aim of this paper was to compare voice and speech characteristics between post-COVID-19 and control subjects. The hypothesis was that acoustic parameters of voice and speech may differentiate subjects infected by COVID-19 from control subjects. Additionally, we expected to observe the persistence of symptoms in women. Methods: In total, 134 subjects participated in the study, were selected for convenience and divided into two groups: 70 control subjects and 64 post-COVID-19 subjects, with an average time of 8.7 months after infection. The recordings were made using the SPIRA software (v.1.0.) on cell phones, based on three verbal tasks: sustained production of the vowel/a/, reading a sentence, and producing a rhyme. Acoustic analyses of speech and voice were carried out with the PRAAT software (v.4.3.18), based on the following parameters: total sentence duration, number of pauses, pause duration, f0, f0SD, jitter, shimmer, and harmonics-to-noise ratio (HNR). Results: Regarding the acoustic characteristics of speech, there were no differences between the groups or between the sexes. Regarding the acoustic characteristics of voice, jitter, shimmer, and HNR, significant differences between the groups were found. Differences between sexes were observed in the following frequency-related parameters: f0, f0SD, and jitter. Conclusions: Some acoustic characteristics of the patients’ voice may show a deteriorated condition even after exacerbation of the disease. These characteristics are compatible with some of the symptoms reported by post-COVID-19 subjects, such as the presence of tension and fatigue. These voice acoustic parameters could be used as biomarkers to screen voice disorders in long-COVID, using artificial intelligence (AI), accelerating the search for diagnosis by specialists. The aim of this paper was to compare voice and speech characteristics between post-COVID-19 and control subjects. The hypothesis was that acoustic parameters of voice and speech may differentiate subjects infected by COVID-19 from control subjects. Additionally, we expected to observe the persistence of symptoms in women.BACKGROUND/OBJECTIVESThe aim of this paper was to compare voice and speech characteristics between post-COVID-19 and control subjects. The hypothesis was that acoustic parameters of voice and speech may differentiate subjects infected by COVID-19 from control subjects. Additionally, we expected to observe the persistence of symptoms in women.In total, 134 subjects participated in the study, were selected for convenience and divided into two groups: 70 control subjects and 64 post-COVID-19 subjects, with an average time of 8.7 months after infection. The recordings were made using the SPIRA software (v.1.0.) on cell phones, based on three verbal tasks: sustained production of the vowel/a/, reading a sentence, and producing a rhyme. Acoustic analyses of speech and voice were carried out with the PRAAT software (v.4.3.18), based on the following parameters: total sentence duration, number of pauses, pause duration, f0, f0SD, jitter, shimmer, and harmonics-to-noise ratio (HNR).METHODSIn total, 134 subjects participated in the study, were selected for convenience and divided into two groups: 70 control subjects and 64 post-COVID-19 subjects, with an average time of 8.7 months after infection. The recordings were made using the SPIRA software (v.1.0.) on cell phones, based on three verbal tasks: sustained production of the vowel/a/, reading a sentence, and producing a rhyme. Acoustic analyses of speech and voice were carried out with the PRAAT software (v.4.3.18), based on the following parameters: total sentence duration, number of pauses, pause duration, f0, f0SD, jitter, shimmer, and harmonics-to-noise ratio (HNR).Regarding the acoustic characteristics of speech, there were no differences between the groups or between the sexes. Regarding the acoustic characteristics of voice, jitter, shimmer, and HNR, significant differences between the groups were found. Differences between sexes were observed in the following frequency-related parameters: f0, f0SD, and jitter.RESULTSRegarding the acoustic characteristics of speech, there were no differences between the groups or between the sexes. Regarding the acoustic characteristics of voice, jitter, shimmer, and HNR, significant differences between the groups were found. Differences between sexes were observed in the following frequency-related parameters: f0, f0SD, and jitter.Some acoustic characteristics of the patients' voice may show a deteriorated condition even after exacerbation of the disease. These characteristics are compatible with some of the symptoms reported by post-COVID-19 subjects, such as the presence of tension and fatigue. These voice acoustic parameters could be used as biomarkers to screen voice disorders in long-COVID, using artificial intelligence (AI), accelerating the search for diagnosis by specialists.CONCLUSIONSSome acoustic characteristics of the patients' voice may show a deteriorated condition even after exacerbation of the disease. These characteristics are compatible with some of the symptoms reported by post-COVID-19 subjects, such as the presence of tension and fatigue. These voice acoustic parameters could be used as biomarkers to screen voice disorders in long-COVID, using artificial intelligence (AI), accelerating the search for diagnosis by specialists. The aim of this paper was to compare voice and speech characteristics between post-COVID-19 and control subjects. The hypothesis was that acoustic parameters of voice and speech may differentiate subjects infected by COVID-19 from control subjects. Additionally, we expected to observe the persistence of symptoms in women. In total, 134 subjects participated in the study, were selected for convenience and divided into two groups: 70 control subjects and 64 post-COVID-19 subjects, with an average time of 8.7 months after infection. The recordings were made using the SPIRA software (v.1.0.) on cell phones, based on three verbal tasks: sustained production of the vowel/a/, reading a sentence, and producing a rhyme. Acoustic analyses of speech and voice were carried out with the PRAAT software (v.4.3.18), based on the following parameters: total sentence duration, number of pauses, pause duration, f , f SD, jitter, shimmer, and harmonics-to-noise ratio (HNR). Regarding the acoustic characteristics of speech, there were no differences between the groups or between the sexes. Regarding the acoustic characteristics of voice, jitter, shimmer, and HNR, significant differences between the groups were found. Differences between sexes were observed in the following frequency-related parameters: f , f SD, and jitter. Some acoustic characteristics of the patients' voice may show a deteriorated condition even after exacerbation of the disease. These characteristics are compatible with some of the symptoms reported by post-COVID-19 subjects, such as the presence of tension and fatigue. These voice acoustic parameters could be used as biomarkers to screen voice disorders in long-COVID, using artificial intelligence (AI), accelerating the search for diagnosis by specialists. Background/Objectives: The aim of this paper was to compare voice and speech characteristics between post-COVID-19 and control subjects. The hypothesis was that acoustic parameters of voice and speech may differentiate subjects infected by COVID-19 from control subjects. Additionally, we expected to observe the persistence of symptoms in women. Methods: In total, 134 subjects participated in the study, were selected for convenience and divided into two groups: 70 control subjects and 64 post-COVID-19 subjects, with an average time of 8.7 months after infection. The recordings were made using the SPIRA software (v.1.0.) on cell phones, based on three verbal tasks: sustained production of the vowel/a/, reading a sentence, and producing a rhyme. Acoustic analyses of speech and voice were carried out with the PRAAT software (v.4.3.18), based on the following parameters: total sentence duration, number of pauses, pause duration, f[sub.0] , f[sub.0] SD, jitter, shimmer, and harmonics-to-noise ratio (HNR). Results: Regarding the acoustic characteristics of speech, there were no differences between the groups or between the sexes. Regarding the acoustic characteristics of voice, jitter, shimmer, and HNR, significant differences between the groups were found. Differences between sexes were observed in the following frequency-related parameters: f[sub.0] , f[sub.0] SD, and jitter. Conclusions: Some acoustic characteristics of the patients’ voice may show a deteriorated condition even after exacerbation of the disease. These characteristics are compatible with some of the symptoms reported by post-COVID-19 subjects, such as the presence of tension and fatigue. These voice acoustic parameters could be used as biomarkers to screen voice disorders in long-COVID, using artificial intelligence (AI), accelerating the search for diagnosis by specialists.
Audience	Academic
Author	Martins, Carolina Fernanda Pentean Finger, Marcelo Fernandes-Svartman, Flaviane R. Quirino, José Henrique de Moura Queiroz, Marcelo da Silva, Luana Cristina Santos Gauy, Marcelo Gazzola, Murilo Raposo de Medeiros, Beatriz Sossolete, Leisi Silva Rios, Julia Vasquez Valenci Almeida, Tatiane Cristina de Morais, Viviam Batista Berti, Larissa Cristina
AuthorAffiliation	5 Department of Computer Science, Faculty of Computing and Informatics, Mackenzie Presbyterian University (MACKENZIE), São Paulo 01302-907, SP, Brazil; murilo.gazzola@mackenzie.br 4 Department of Linguistics, Faculty of Philosophy, Language, Literature and Human Sciences University of São Paulo (USP), São Paulo 05508-220, SP, Brazil; biarm@usp.br 2 Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo (USP), São Paulo 05508-220, SP, Brazil; marcelo.gauy@usp.br (M.G.); mqz@ime.usp.br (M.Q.); mfinger@ime.usp.br (M.F.) 3 Department of Classical and Vernacular Literature, Faculty of Philosophy, Language, Literature and Human Sciences, University of São Paulo (USP), São Paulo 05508-220, SP, Brazil; flavianesvartman@usp.br 1 Departament of Speech, Language and Hearing Sciences, Faculty of Philosophy and Science, São Paulo State University (UNESP), Marília 17525-900, SP, Brazil; luana.cristina@unesp.br (L.C.S.d.S.); julia.vasquez@unesp.br (J.V.V.R.); v.m
AuthorAffiliation_xml	– name: 2 Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo (USP), São Paulo 05508-220, SP, Brazil; marcelo.gauy@usp.br (M.G.); mqz@ime.usp.br (M.Q.); mfinger@ime.usp.br (M.F.) – name: 1 Departament of Speech, Language and Hearing Sciences, Faculty of Philosophy and Science, São Paulo State University (UNESP), Marília 17525-900, SP, Brazil; luana.cristina@unesp.br (L.C.S.d.S.); julia.vasquez@unesp.br (J.V.V.R.); v.morais@unesp.br (V.B.M.); tatiane.almeida@unesp.br (T.C.d.A.); leisi.sossolote@unesp.br (L.S.S.); jose.quirino@bp.org.br (J.H.d.M.Q.); carolina.pentean@unesp.br (C.F.P.M.) – name: 4 Department of Linguistics, Faculty of Philosophy, Language, Literature and Human Sciences University of São Paulo (USP), São Paulo 05508-220, SP, Brazil; biarm@usp.br – name: 3 Department of Classical and Vernacular Literature, Faculty of Philosophy, Language, Literature and Human Sciences, University of São Paulo (USP), São Paulo 05508-220, SP, Brazil; flavianesvartman@usp.br – name: 5 Department of Computer Science, Faculty of Computing and Informatics, Mackenzie Presbyterian University (MACKENZIE), São Paulo 01302-907, SP, Brazil; murilo.gazzola@mackenzie.br
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Cites_doi	10.3389/fmed.2021.750378 10.1172/JCI118067 10.1016/j.mayocp.2020.07.024 10.1136/bcr-2020-238419 10.1121/10.0016848 10.1089/jwh.2021.0411 10.1016/j.cmi.2021.11.002 10.1016/j.eclinm.2020.100683 10.1002/jmv.26368 10.1016/j.mayocp.2021.11.033 10.17576/gema-2023-2303-03 10.1044/2014_JSLHR-S-13-0283 10.1007/s00405-021-07217-2 10.1016/j.jvoice.2020.09.024 10.1016/j.amjoto.2023.103950 10.1038/s41586-020-2700-3 10.1016/j.apmr.2020.05.006 10.21437/SpeechProsody.2022-43 10.1007/s00405-022-07468-7 10.5001/omj.2021.91 10.1007/s12070-021-02951-9 10.1186/s12913-020-06001-y 10.3390/diagnostics12081839 10.3390/jcm9092946
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Keywords	voice phonetics speech acoustics adult post-acute COVID-19 syndrome
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Snippet	Background/Objectives: The aim of this paper was to compare voice and speech characteristics between post-COVID-19 and control subjects. The hypothesis was... The aim of this paper was to compare voice and speech characteristics between post-COVID-19 and control subjects. The hypothesis was that acoustic parameters...
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SubjectTerms	Acoustics Age groups Antigens Artificial intelligence Biomarkers Brain research COVID-19 Cytokines Fatigue Health aspects Hospitalization Infections Long COVID Respiratory failure Speaking Speech Voice
Title	Acoustic Characteristics of Voice and Speech in Post-COVID-19
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