Concern for mental health has increased in the last years due to its impact in people life quality and its consequential effect on healthcare systems. Automatic systems that can help in the diagnosis, symptom monitoring, alarm generation etc. are an emerging technology that has provided several challenges to the scientific community. The goal of this work is to design a system capable of distinguishing between healthy and depressed and/or anxious subjects, in a realistic environment, using their speech. The system is based on efficient representations of acoustic signals and text representations extracted within the self-supervised paradigm. Considering the good results achieved by using acoustic signals, another set of experiments was carried out in order to detect the specific illness. An analysis of the emotional information and its impact in the presented task is also tackled as an additional contribution.