  Stress is a major threat to well-being that manifests in a variety of
physiological and mental symptoms. Utilising speech samples collected while the
subject is undergoing an induced stress episode has recently shown promising
results for the automatic characterisation of individual stress responses. In
this work, we introduce new findings that shed light onto whether speech
signals are suited to model physiological biomarkers, as obtained via cortisol
measurements, or self-assessed appraisal and affect measurements. Our results
show that different indicators impact acoustic features in a diverse way, but
that their complimentary information can nevertheless be effectively harnessed
by a multi-tasking architecture to improve prediction performance for all of
them.
