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Learning Voice Source Related Information for Depression Detection
2019
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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 for
doi:10.1109/icassp.2019.8683498
dblp:conf/icassp/DubaguntaVM19
fatcat:c574s66e5jejlc2szzwmjzm2pm