Title: Speech signals as biomarkers: using glottal features for non-invasive COVID-19 testing

Authors: Girish Gidaye; Abhay Barage; Nirmayee Dighe; Kadria Ezzine; Varsha Turkar; Gajanan Nagare

Addresses: Department of Electronics and Computer Science, Vidyalankar Institute of Technology, Mumbai, India ' Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, 1300 York Avenue, New York, NY 10065, USA ' Department of Information Systems, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA ' University of Lorraine, Unnamed Road, 54000 Nancy, France ' Electronics and Telecommunication Engineering Department, Vidyalankar Institute of Technology, Mumbai, India ' Department of Biomedical Engineering, Vidyalankar Institute of Technology, Mumbai, India

Abstract: The COVID-19 pandemic was the most significant global health crisis in recent history, with lasting impacts on societies worldwide. Current screening methods are invasive, slow, frequently inaccurate, and limited in capacity. To overcome these limitations researchers used conventional features extracted from speech signals. In the proposed methodology, the change in vibratory pattern due to COVID-19 is captured by extracting glottal features from glottal signal acquired by inverse filtering. Various machine learning models like naïve Bayes, ADABOOST, gradient boost, decision tree, support vector machine (SVM), stochastic gradient descent (SGD), K-nearest neighbours, and random forest (RF) are used in this study. It is observed that the gradient boost gives the maximum classification accuracy (99.97%) for COVID-19 detection and SGD gives the maximum classification accuracy (99.76%) for severity grading. It is also observed that the time instants glottal features outperform frequency domain and model-based features. The Coswara database is used for this study.

Keywords: COVID-19; glottal signal; glottal features; speech signal; machine learning; quasi-closed phase; QCP; support vector machine; SVM; stochastic gradient descent; SGD.

DOI: 10.1504/IJBET.2025.143755

International Journal of Biomedical Engineering and Technology, 2025 Vol.47 No.1, pp.65 - 85

Received: 11 May 2024
Accepted: 22 Jul 2024
Published online: 06 Jan 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article