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
Departament of Speech, Language and Hearing Sciences, Faculty of Philosophy and Science, São Paulo State University (UNESP), Marília 17525-900, SP, Brazil.
Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo (USP), São Paulo 05508-220, SP, Brazil.
Healthcare (Basel). 2025 Jan 1;13(1):63. doi: 10.3390/healthcare13010063.
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.
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, fSD, 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, fSD, 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.
背景/目的：本文旨在比较新冠康复者与对照组受试者的嗓音和言语特征。假设是嗓音和言语的声学参数可能会区分新冠感染者与对照组受试者。此外，我们期望观察女性症状的持续性。
共有134名受试者参与了本研究，为方便起见进行选择并分为两组：70名对照组受试者和64名新冠康复者，感染后平均时间为8.7个月。使用手机上的SPIRA软件（版本1.0）进行录音，基于三项言语任务：持续发元音/a/、朗读句子和押韵。使用PRAAT软件（版本4.3.18）对言语和嗓音进行声学分析，基于以下参数：句子总时长、停顿次数、停顿时长、基频（f）、基频标准差（fSD）、抖动、闪烁和信噪比（HNR）。
关于言语的声学特征，两组之间以及两性之间均无差异。关于嗓音的声学特征，抖动、闪烁和信噪比方面，两组之间存在显著差异。在以下与频率相关的参数中观察到了两性差异：基频、基频标准差和抖动。
即使在疾病加重后，患者嗓音的一些声学特征可能仍显示出恶化状况。这些特征与新冠康复者报告的一些症状相符，如紧张和疲劳的存在。这些嗓音声学参数可用作生物标志物，利用人工智能（AI）筛查长期新冠患者的嗓音障碍，加快专科医生的诊断搜索。
