Respiratory Distress Detection from Telephone Speech using Acoustic and Prosodic Features

TL;DR

This study explores automatic detection of respiratory distress from telephone speech using acoustic and prosodic features, achieving promising accuracy and identifying key speech indicators linked to respiratory issues.

Contribution

It introduces a novel approach combining acoustic and prosodic features with SVM for respiratory distress detection in telemedicine speech data.

Findings

Achieved 86.4% accuracy in detecting respiratory distress.

Identified loudness, voice rate, and pause duration as key features.

Demonstrated feasibility of remote respiratory assessment via speech analysis.

Abstract

With the widespread use of telemedicine services, automatic assessment of health conditions via telephone speech can significantly impact public health. This work summarizes our preliminary findings on automatic detection of respiratory distress using well-known acoustic and prosodic features. Speech samples are collected from de-identified telemedicine phonecalls from a healthcare provider in Bangladesh. The recordings include conversational speech samples of patients talking to doctors showing mild or severe respiratory distress or asthma symptoms. We hypothesize that respiratory distress may alter speech features such as voice quality, speaking pattern, loudness, and speech-pause duration. To capture these variations, we utilize a set of well-known acoustic and prosodic features with a Support Vector Machine (SVM) classifier for detecting the presence of respiratory distress. Experimental evaluations are performed using a 3-fold cross-validation scheme, ensuring patient-independent data splits. We obtained an overall accuracy of 86.4\% in detecting respiratory distress from the speech recordings using the acoustic feature set. Correlation analysis reveals that the top-performing features include loudness, voice rate, voice duration, and pause duration.