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.

本文利用面部特征提取技术，通过多种视听方式设计自动检测方法，获取心理学标准 PHQ-8 测试结果，性能表现最佳的为面部特征提取，平均绝对误差为 4.66，语言特征稍高，为 5.17，而从音频记录中提取的 Turn Features 在测试集中表现最佳，平均绝对误差为 4.11，该体系结构在 AVEC 2017 抑郁症子挑战赛中成为优胜者。