10 research outputs found
COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis
At the time of writing, the world population is suffering from more than
10,000 registered COVID-19 disease epidemic induced deaths since the outbreak
of the Corona virus more than three months ago now officially known as
SARS-CoV-2. Since, tremendous efforts have been made worldwide to counter-steer
and control the epidemic by now labelled as pandemic. In this contribution, we
provide an overview on the potential for computer audition (CA), i.e., the
usage of speech and sound analysis by artificial intelligence to help in this
scenario. We first survey which types of related or contextually significant
phenomena can be automatically assessed from speech or sound. These include the
automatic recognition and monitoring of breathing, dry and wet coughing or
sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain to
name but a few. Then, we consider potential use-cases for exploitation. These
include risk assessment and diagnosis based on symptom histograms and their
development over time, as well as monitoring of spread, social distancing and
its effects, treatment and recovery, and patient wellbeing. We quickly guide
further through challenges that need to be faced for real-life usage. We come
to the conclusion that CA appears ready for implementation of (pre-)diagnosis
and monitoring tools, and more generally provides rich and significant, yet so
far untapped potential in the fight against COVID-19 spread
An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety
The COVID-19 outbreak was announced as a global pandemic by the World Health
Organisation in March 2020 and has affected a growing number of people in the
past few weeks. In this context, advanced artificial intelligence techniques
are brought to the fore in responding to fight against and reduce the impact of
this global health crisis. In this study, we focus on developing some potential
use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In
particular, by analysing speech recordings from these patients, we construct
audio-only-based models to automatically categorise the health state of
patients from four aspects, including the severity of illness, sleep quality,
fatigue, and anxiety. For this purpose, two established acoustic feature sets
and support vector machines are utilised. Our experiments show that an average
accuracy of .69 obtained estimating the severity of illness, which is derived
from the number of days in hospitalisation. We hope that this study can foster
an extremely fast, low-cost, and convenient way to automatically detect the
COVID-19 disease