New tech allegedly detects Covid-19 through voice analysis

Big implications.


By Ken Macon: September 5, 2022



Dutch scientists say they have developed technology that is said to highlight those infected with COVID-19 by scanning their voice, according to Imperial College London. [Ed: but we all know just how reliable they are]


It has a reported accuracy of 89% for positive cases and 83% for negative cases.

Aside from supposedly being more accurate than lateral flow tests, the technology can detect the virus in less than a minute.


The researchers said they decided to develop the software because Covid-19 affects the vocal cords and the upper respiratory tract, meaning it changes a person’s voice.

In a world where some people found to be carrying the virus are forced into quarantine, the technology has major implications.


Researchers used data from University of Cambridge’s crowdsourcing COVID-19 Sounds App, using 893 audio samples from over 4,000 participants.


Using a voice analysis technique called Mel-Spectrogram analysis, the researchers used an artificial intelligence model to distinguish voices of people with Covid-19 and those without. A model called Long-Short Term Memory (LSTM) provided the best results.

“These promising results suggest that simple voice recordings and fine-tuned AI algorithms can potentially achieve high precision in determining which patients have COVID-19 infection,” said Wafaa Aljbawi, one of the researchers.


“Such tests can be provided at no cost and are simple to interpret”.

“Moreover, they enable remote, virtual testing and have a turnaround time of less than a minute”.

“They could be used, for example, at the entry points for large gatherings, enabling rapid screening of the population”.


“These results show a significant improvement in the accuracy of diagnosing COVID-19 compared to state-of-the-art tests such as the lateral flow test.”

“However, since this test is virtually free, it is possible to invite people for PCR tests if the LSTM tests show they are positive.”

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