A paper submitted in collaboration with Fuxiang Tao and Anna Exposition has been accepted for presentation at Interspeech 2020 (the conference is rated A by CORE):
F.Tao, A.Esposito and A.Vinciarelli, “Spotting the traces of depression in read speech: An Approach Based on Computational Paralinguistics and Social Signal Processing“, Proceedings of Interspeech, 2020.
The most interesting aspect of the article is that it uses a classifier to show that three particular behavioural cues (reading speed, number of pauses and average length of the pauses) can improve by close to 20 points the accuracy of classifier that discriminates between depressed and non-depressed readers. In other words, the paper proposes the use of a classifier as a means to test the association between depression and behavioural cues, in alternative to the common approaches used in psychology for the same purpose (e.g., correlational analysis). In addition, the paper shows that neuroscience findings, in particular the interplay between depression and neural mechanisms underlying language processing, can help to improve technology.