  Depression is a major debilitating disorder which can affect people from all
ages. With a continuous increase in the number of annual cases of depression,
there is a need to develop automatic techniques for the detection of the
presence and extent of depression. In this AVEC challenge we explore different
modalities (speech, language and visual features extracted from face) to design
and develop automatic methods for the detection of depression. In psychology
literature, the PHQ-8 questionnaire is well established as a tool for measuring
the severity of depression. In this paper we aim to automatically predict the
PHQ-8 scores from features extracted from the different modalities. We show
that visual features extracted from facial landmarks obtain the best
performance in terms of estimating the PHQ-8 results with a mean absolute error
(MAE) of 4.66 on the development set. Behavioral characteristics from speech
provide an MAE of 4.73. Language features yield a slightly higher MAE of 5.17.
When switching to the test set, our Turn Features derived from audio
transcriptions achieve the best performance, scoring an MAE of 4.11
(corresponding to an RMSE of 4.94), which makes our system the winner of the
AVEC 2017 depression sub-challenge.
