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

Andreas Triantafyllopoulos, Markus Fendler, Anton Batliner, Maurice Gerczuk, Shahin Amiriparian, Thomas M. Berghaus, Björn W. Schuller

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

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.

Original languageEnglish
Pages (from-to)3623-3627
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2022-September
DOIs
StatePublished - 2022
Externally publishedYes
Event23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022 - Incheon, Korea, Republic of
Duration: 18 Sep 202222 Sep 2022

Keywords

  • COPD
  • digital health
  • feature interpretation
  • pathological speech
  • personalisation

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