Depression is a disease that affects a considerable portion of the world population. Severe cases of depression interfere with the common live of patients, for those patients a strict monitoring is necessary in order to control the progress of the disease and to prevent undesired side effects. A way to keep track of patients with depression is by means of online monitoring via human-computer-interaction. The AVEC'14 challenge aims at developing technology towards the online monitoring of depression patients. This paper describes an approach to depression recognition from audiovisual information in the context of the AVEC'14 challenge. The proposed method relies on an effective voice segmentation procedure, followed by segment-level feature extraction and aggregation. Finally, a meta-model is trained to fuse mono-modal information. The main novel features of our proposal are that (1) we use affective dimensions for building depression recognition models; (2) we extract visual information from voice and silence segments separately; (3) we consolidate features and use a meta-model for fusion. The proposed methodology is evaluated, experimental results reveal the method is competitive.