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Assessing depression via speech characteristics is a growing area of interest in quantitative mental health research with a view to a clinical mental health assessment tool. As a mood disorder, depression induces changes in response to emotional stimuli, which motivates this investigation into the relationship between emotion and depression affected speech. This paper investigates how emotional information expressed in speech (i.e. arousal, valence, dominance) contributes to the classification of minimally depressed and moderately-severely depressed individuals. Experiments based on a subset of the AVEC 2014 database show that manual emotion ratings alone are discriminative of depression and combining rating-based emotion features with acoustic features improves classification between mild and severe depression. Emotion-based data selection is also shown to provide improvements in depression classification and a range of threshold methods are explored. Finally, the experiments presented demonstrate that automatically predicted emotion ratings can be incorporated into a fully automatic depression classification to produce a 5% accuracy improvement over an acoustic-only baseline system.