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Automatic classification of depression using audiovisual cues can help towards its objective diagnosis. In this paper, we present a multimodal depression classification system as a part of the 2016 Audio/Visual Emotion Challenge and Workshop (AVEC2016). We investigate a number of audio and video features for classification with different fusion techniques and temporal contexts. In the audio modality, Teager energy cepstral coefficients (TECC) outperform standard baseline features; while thedoi:10.1145/2988257.2988261 dblp:conf/mm/NasirJSCG16 fatcat:33geyegcgfcclgnmufbc5f32iq