  Major Depressive Disorder (MDD) is a common worldwide mental health issue
with high associated socioeconomic costs. The prediction and automatic
detection of MDD can, therefore, make a huge impact on society. Speech, as a
non-invasive, easy to collect signal, is a promising marker to aid the
diagnosis and assessment of MDD. In this regard, speech samples were collected
as part of the Remote Assessment of Disease and Relapse in Major Depressive
Disorder (RADAR-MDD) research programme. RADAR-MDD was an observational cohort
study in which speech and other digital biomarkers were collected from a cohort
of individuals with a history of MDD in Spain, United Kingdom and the
Netherlands. In this paper, the RADAR-MDD speech corpus was taken as an
experimental framework to test the efficacy of a Sequence-to-Sequence model
with a local attention mechanism in a two-class depression severity
classification paradigm. Additionally, a novel training method, HARD-Training,
is proposed. It is a methodology based on the selection of more ambiguous
samples for the model training, and inspired by the curriculum learning
paradigm. HARD-Training was found to consistently improve - with an average
increment of 8.6% - the performance of our classifiers for both of two speech
elicitation tasks used and each collection site of the RADAR-MDD speech corpus.
With this novel methodology, our Sequence-to-Sequence model was able to
effectively detect MDD severity regardless of language. Finally, recognising
the need for greater awareness of potential algorithmic bias, we conduct an
additional analysis of our results separately for each gender.
