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

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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, Jose Henrique de Moura ; Martins, Carolina Fernanda Pentean ; Fernandes-Svartman, Flaviane R. ; de Medeiros, Beatriz Raposo ; Queiroz, Marcelo ; Gazzola, Murilo ; Finger, Marcelo

• Document type: Journal article
• Source: HEALTHCARE; v. 13, n. 1, p. 14-pg., 2025-01-01.

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. (AU)

• FAPESP's process: 23/00488-5 - SPIRA-BM: biomarkers for respiratory conditions on mobile devices using audio analysis with artificial intelligence
• Grantee: Marcelo Finger
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 22/16374-6 - Pretrained Neural Network models for detecting respiratory conditions through audio analysis
• Grantee: Marcelo Matheus Gauy
• Support Opportunities: Scholarships in Brazil - Post-Doctoral