3 accepted papers
This article shows that an attention mechanism, the Multi-Local Attention, can improve a depression detection approach based on Long Short-Term Memory Networks. Besides leading to higher performance metrics (e.g., Accuracy and F1 Score), Multi-Local Attention improves two other aspects of the approa…
The computing community is making major efforts towards automatic detection of depression, a serious pathology that affects roughly 4.4% of the world’s population. One of the main difficulties is the collection of data aimed at training models capable to learn differences between depressed and non-d…
This article proposes an automatic approach - based on nonverbal speech features - aimed at the automatic discrimination between depressed and non-depressed speakers. The experiments have been performed over one of the largest corpora collected for such a task in the literature (62 patients diagnose…