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During depression neurophysiological changes can occur, which may affect laryngeal control i.e. behaviour of the vocal folds. Characterising these changes in a precise manner from speech signals is a non trivial task, as this typically involves reliable separation of the voice source information from them. In this paper, by exploiting the abilities of CNNs to learn task-relevant information from the input raw signals, we investigate several methods to model voice source related information fordoi:10.1109/icassp.2019.8683498 dblp:conf/icassp/DubaguntaVM19 fatcat:c574s66e5jejlc2szzwmjzm2pm