Every employed individual has been guilty of calling in sick without actually being sick. It was all fun and games, until now. Researchers from Sardar Vallabhbhai National Institute of Technology, Surat and the Rhenish University of Applied Science, Germany, have conducted a study on developing speech signal-based non-invasive diagnosis techniques in the field of biomedical signal processing. The study aimed to develop a method that can identify a person with a common cold from their speech with higher performance and fewer features. The objective of the study was to detect viral infections and similar illnesses with comparable symptoms to prevent the spread of these diseases and remotely monitor patient health.
The researchers found that the way people talk when they have a cold is different from when they don’t. They came up with three things to help figure this out: Normalized Harmonic Peak with respect to the First Harmonic Peak (NHPF), Normalized Harmonic Peak with respect to the Maximum value of Harmonic Peak (NHPM), and Successive Harmonic Peak Ratio (SHPR). NHPF and NHPM show how loud different sounds are compared to the first and loudest sound, while SHPR compares the loudness of different sounds to the ones next to them. The classifiers look at the scores for each sound and decide if it sounds like someone with a cold or not. Then, they add up all the scores for each sound and decide which one it sounds more like overall.
The study found that the new features they proposed were good at telling the difference between the speech of someone with a cold and someone without. The features accurately classified cold and non-cold speech with scores of 69.16 percent and 66.90 percent respectively. This means that the new technique could help identify people with colds or similar illnesses automatically, which could be helpful for doctors who want to monitor patients’ health.
The researchers believe that this study’s outcome could be beneficial in preventing the spread of viral infections and monitoring patient health remotely. Non-invasive diagnosis techniques like this could help clinicians diagnose illnesses and suggest treatments without requiring the patient to come to the clinic physically. This could be particularly useful in remote areas where people do not have easy access to healthcare facilities. However, the researchers also caution that further research is necessary to develop more comprehensive techniques that can detect a broader range of illnesses accurately.
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