  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.
