Voice based pathology detection from respiratory sounds using optimized classifiers

Abstract

Speech is an important tool for communication. When a person speaks, the vocal cords come closer and the glottis is partially closed. The airflow which passes through glottis is disturbed by vocal cords and speech waveform is produced. The person who suffers from the vocal cord paralysis or vocal cord blister, his lungs are filled with fluid and airway blockage cannot generate a similar waveform as a healthy person. In this work, we compare traditional approaches with deep learning based approaches for respiratory disease detection to distinguish between a healthy person and the victim of pathological voice disorder. Four conventional machine learning classifiers and a one-dimensional convolution neural network based classifier have been implemented on two benchmark datasets ICBHI 2017 and Coswara. Our experiments show that the CNN based approach and Random Forest algorithm exhibit superior performance over other approaches on ICBHI 2017 and Coswara datasets, respectively.