Soft biometrics comprises the biological traits that are not sufficient for person authentication but can help to narrow the search space. Evidence of mental health state can be considered as a soft biometric, as it provides valuable information about the identity of an individual. Different approaches have been used for the automatic classification of speech in “depressed” or “non-depressed”, but the differences in algorithms, features, databases and performance measures make it difficult to draw conclusions about which features and techniques are more suitable for this task. In this work, the performance of different acoustic features for classification of depression in speech was studied in the framework of the audiovisual emotion challenge (AVEC 2013). To do so, an approach in which the audio data is segmented and projected into a total variability subspace was used, and these projected data was used to estimate the depression level by cosine distance scoring and majority voting.