  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.
