(untitled) (Nonlinear Studies, 2024)
Respiratory Disorder detection using speech analysis is a vital research topic in today’s scenario, which is reliable, easy to use, economic, and efficient, and also helps in the detection of disorder in the early stage. The purpose of this article is to investigate whether or not speech parameters obtained can be utilized for envisaging respiratory disorders using predictive machine learning techniques, and to explore the role of different machine learning components such as data division protocols and classification to determine suitable speech parameters for detection of respiratory disorders. In this work, extraction and evaluation of speech parameters were done using PRAAT software. A dataset consisting of speech recordings and spirometry data was used in the experiment. This article used Mel Frequency Cepstral Coefficient (MFCC) and Linear Predictive Coding (LPC) coefficients for speech analysis. Two machine learning models, Support vector machine and Naïve Bayes have been employed and compared for the assessment of respiratory disorders. The results showed that SVM achieved the highest classification accuracy of 100% for LPC and 83.3% for MFCC utilizing the holdout method. While Naïve Bayes achieved the highest accuracy of 85% for LPC and 70% for MFCC using the 10-fold method. The results obtained indicated that the MFCC and LPC with high efficiency may provide an aid in the simple assessment of respiratory disorders with proposed classifiers.