Goal: We propose a speech modeling and signal-processing framework to detect and track COVID-19 through asymptomatic and symptomatic stages. Methods: The approach is based on complexity of neuromotor coordination across speech subsystems involved in respiration, phonation and articulation, motivated by the distinct nature of COVID-19 involving lower (i.e., bronchial tubes, diaphragm, lower trachea) versus upper (i.e., laryngeal, pharyngeal, oral and nasal) respiratory tract inflammation [1], as well as by the growing evidence of the virus' neurological manifestations [2]—[5]. Preliminary results: An exploratory study with audio interviews of five subjects provides Cohen's d effect sizes between pre-COVID-19 (pre-exposure) from post-COVID-19 (after positive diagnosis but asymptomatic) using: coordination of respiration (as measured through acoustic waveform amplitude) and laryngeal motion (fundamental frequency and cepstral peak prominence), and coordination of laryngeal and articulatory (formant center frequencies) motion. Conclusions: While there is a strong subject-dependence, the group-level morphology of effect sizes indicates a reduced complexity of subsystem coordination. Validation is needed with larger more controlled datasets and to address confounding influences such as different recording conditions, unbalanced data quantities, and changes in underlying vocal status from pre-to-post time recordings.

中文翻译：

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基于语音生成子系统协调的 COVID-19 生物标志物框架

目标：我们提出了一个语音建模和信号处理框架，通过无症状和有症状阶段检测和跟踪 COVID-19。方、咽、口腔和鼻腔）呼吸道炎症 [1]，以及越来越多的证据表明病毒的神经系统表现 [2]-[5]。初步结果：一项对五名受试者进行音频采访的探索性研究提供了 Cohen 在 COVID-19 前（暴露前）与 COVID-19 后（阳性诊断后但无症状）之间的 d 效应大小，使用：呼吸的协调（通过声波振幅测量）和喉部运动（基频和倒谱峰突出），以及喉部和关节运动（共振峰中心频率）的协调。结论：虽然存在很强的学科依赖性，但效果大小的组级形态表明子系统协调的复杂性降低。需要对更大、更受控制的数据集进行验证，并解决混杂影响，例如不同的录音条件、不平衡的数据量以及从前后时间录音中潜在的声音状态的变化。虽然有很强的主题依赖性，但效果大小的组级形态表明子系统协调的复杂性降低。需要对更大、更受控制的数据集进行验证，并解决诸如不同的录音条件、不平衡的数据量以及从前后时间录音中潜在的声音状态变化等混杂影响。虽然有很强的主题依赖性，但效果大小的组级形态表明子系统协调的复杂性降低。需要对更大、更受控制的数据集进行验证，并解决诸如不同的录音条件、不平衡的数据量以及从前后时间录音中潜在的声音状态变化等混杂影响。