It’s often easy to tell when colleagues are struggling with a cold—they sound sick. Maybe their voices are lower or have a nasal tone.
Infections change the quality of our voices in various ways.
In a new study, researchers found these changes in COVID-19 patients even when these changes are too subtle for people to hear or even notice in themselves.
By processing speech recordings of people infected with COVID-19 but not yet showing symptoms, these researchers found evidence of vocal biomarkers, or measurable indicators, of the disease.
These biomarkers stem from disruptions the infection causes in the movement of muscles across the respiratory, laryngeal, and articulatory systems.
While this research is still in its early stages, the initial findings lay a framework for studying these vocal changes in greater detail.
This work may also hold promise for using mobile apps to screen people for the disease, particularly those who are asymptomatic.
The research was conducted by MIT Lincoln Laboratory scientists.
Previous research has shown vocal biomarkers of neurological disorders such as amyotrophic lateral sclerosis (ALS) and Parkinson’s disease.
These diseases, and many others, change the brain’s ability to turn thoughts into words, and those changes can be detected by processing speech signals.
In the study, the team wondered whether vocal biomarkers might also exist for COVID-19. The symptoms led them to think so.
When symptoms manifest, a person typically has difficulty breathing. Inflammation in the respiratory system affects the intensity with which air is exhaled when a person talks.
This air interacts with hundreds of other potentially inflamed muscles on its journey to speech production.
These interactions impact the loudness, pitch, steadiness, and resonance of the voice—measurable qualities that form the basis of their biomarkers.
The team combed YouTube for clips of celebrities or TV hosts who had given interviews while they were COVID-19 positive but asymptomatic. They identified five people.
Then, they downloaded interviews of those people from before they had COVID-19, matching audio conditions as best they could.
They then used algorithms to extract features from the vocal signals in each audio sample.
These vocal features serve as proxies for the underlying movements of the speech production systems.
The signal’s amplitude, or loudness, was extracted as a proxy for movement in the respiratory system.
For studying movements in the larynx, they measured pitch and the steadiness of pitch, two indicators of how stable the vocal cords are.
As a proxy for articulator movements—like those of the tongue, lips, jaw, and more—they extracted speech formants.
Speech formants are frequency measurements that correspond to how the mouth shapes sound waves to create a sequence of phonemes (vowels and consonants) and to contribute to a certain vocal quality (nasally versus warm, for example).
They hypothesized that COVID-19 inflammation causes muscles across these systems to become overly coupled, resulting in a less complex movement.
The researchers looked for evidence of coupling in their features, measuring how each feature changed in relation to another in 10-millisecond increments as the subject spoke.
These values were then plotted on an eigenspectrum; the shape of this eigenspectrum plot indicates the complexity of the signals.
In the end, they found a decreased complexity of movement in the COVID-19 interviews as compared to the pre-COVID-19 interviews.
These preliminary results hint that biomarkers derived from vocal system coordination can indicate the presence of COVID-19.
However, the researchers note that it’s still early to draw conclusions, and more data are needed to validate their findings.
They’re working now with a publicly released dataset from Carnegie Mellon University that contains audio samples from individuals who have tested positive for COVID-19.
One author of the study is Thomas Quatieri, a senior staff member in the laboratory’s Human Health and Performance Systems Group.
The study is published in IEEE Open Journal of Engineering in Medicine and Biology.
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