Three groups of subjects (ADSD, ALS, and Tremor) were analyzed by the Motor Speech Profile System (Kay Elemetrics, Lincoln Park, NJ) for fundamental frequency (Fo), standard deviation of Fo, diadochokinetic rate (ddk), standard deviation of ddk, mean intensity and standard deviation of ddk, frequency and amplitude variability in connected speech, and speaking rate in connected speech. Profiles of the three groups are presented with the significant features that differentiated one from the other.In March, as the staggering scope of the coronavirus pandemic started to become clear, officials around the world began enlisting the public to join in the fight. Hospitals asked local companies to donate face masks. Researchers called on people who had recovered from COVID-19 to donate their blood plasma. And in Israel, the defence ministry and a start-up company called Vocalis Health asked people to donate their voices. Vocalis, a voice-analysis company with offices in Israel and the United States, had previously built a smartphone app that could detect flare-ups of chronic obstructive pulmonary disease by listening for signs that users were short of breath when speaking. The firm wanted to do the same thing with COVID-19. People who had tested positive for the coronavirus could participate simply by downloading a Vocalis research app. Once a day, they fired up the app and spoke into their phones, describing an image aloud and counting from 50 to 70. Then Vocalis began processing these recordings with its machine-learning system, alongside the voices of people who had tested negative for the disease, in an attempt to identify a voiceprint for the illness. By mid-summer, the firm had more than 1,500 voice samples and a pilot version of a digital COVID-19 screening tool. The tool, which the company is currently testing around the world, is not intended to provide a definitive diagnosis, but to help clinicians triage potential cases, identifying people who might be most in need of testing, quarantine or in-person medical care. “Can we help with our AI algorithm?” asks Tal Wenderow, the president and chief executive of Vocalis. “This is not invasive, it’s not a drug, we’re not changing anything. They’re not the only ones racing to find vocal biomarkers of COVID-19 - at least three other research groups are working on similar projects. Other teams are analysing audio recordings of COVID-19 coughs and developing voice-analysis algorithms designed to detect when someone is wearing a face mask. It’s a sign of how hungry the young field of vocal diagnostics is to make its mark. Over the past decade, scientists have used artificial intelligence (AI) and machine-learning systems to identify potential vocal biomarkers of a wide variety of conditions, including dementia, depression, autism spectrum disorder and even heart disease. The technologies they have developed are capable of picking out subtle differences in how people with certain conditions speak, and companies around the world are beginning to commercialize them.įor now, most teams are taking a slow, stepwise approach, designing tailored tools for use in doctors’ offices or clinical trials.

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