Distinguishing between pre- and post-treatment in the speech of patients with chronic obstructive pulmonary disease

TL;DR

This study demonstrates that personalized speech feature normalization can effectively distinguish pre- and post-treatment states in COPD patients, offering a potential tool for monitoring disease progression and treatment efficacy.

Contribution

The paper introduces a novel speech analysis approach with speaker-level normalization to differentiate COPD treatment stages, linking features to pathological voice properties.

Findings

Achieved up to 82% UAR in classifying treatment stages

Identified key speech features linked to COPD pathology

Enabled auditory interpretation of treatment effects

Abstract

Chronic obstructive pulmonary disease (COPD) causes lung inflammation and airflow blockage leading to a variety of respiratory symptoms; it is also a leading cause of death and affects millions of individuals around the world. Patients often require treatment and hospitalisation, while no cure is currently available. As COPD predominantly affects the respiratory system, speech and non-linguistic vocalisations present a major avenue for measuring the effect of treatment. In this work, we present results on a new COPD dataset of 20 patients, showing that, by employing personalisation through speaker-level feature normalisation, we can distinguish between pre- and post-treatment speech with an unweighted average recall (UAR) of up to 82\,\% in (nested) leave-one-speaker-out cross-validation. We further identify the most important features and link them to pathological voice properties, thus enabling an auditory interpretation of treatment effects. Monitoring tools based on such approaches may help objectivise the clinical status of COPD patients and facilitate personalised treatment plans.