Drafts by Filomena Scibelli
This article proposes an automatic approach-based on non-verbal speech features-aimed at the auto... more This article proposes an automatic approach-based on non-verbal 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 diagnosed with depression and 54 healthy control subjects), especially when it comes to data where the depressed speakers have been diagnosed as such by professional psychiatrists. The results show that the discrimination can be performed with an accuracy of over 75% and the error analysis shows that the chances of correct classification do not change according to gender, depression-related pathology diagnosed by the psychiatrists or length of the pharmacological treatment (if any). Furthermore, for every depressed subject, the corpus includes a control subject that matches age, education level and gender. This ensures that the approach actually discriminates between depressed and non depressed speakers and does not simply capture differences resulting from other factors.
—This work investigates the ability of deaf subjects to correctly label foreign emotional faces o... more —This work investigates the ability of deaf subjects to correctly label foreign emotional faces of happiness, sadness, surprise, anger, fear, and disgust, in comparison with typically hearing ones. The experiment involved 14 deaf (signing) and 14 hearing subjects matched by age and gender. The emotional faces were selected from the Radboud Database. The results show significant difference between the two groups, with deaf performing significantly poorly in the decoding accuracy and intensity ratings of disgust, surprise, and anger. Considerations are made on the effects of the social and cultural context to leverage the universality of emotional facial expressions.
Papers by Filomena Scibelli
Most studies investigating the processing of emotions in depressed patients reported impairments ... more Most studies investigating the processing of emotions in depressed patients reported impairments in the decoding of negative emotions. However, these studies adopted static stimuli (mostly stereotypical facial expressions corresponding to basic emotions) which do not reflect the way people experience emotions in everyday life. For this reason, this work proposes to investigate the decoding of emotional expressions in patients affected by recurrent major depressive disorder (RMDD) using dynamic audio/ video stimuli. RMDDs' performance is compared with the performance of patients with adjustment disorder with depressed mood (ADs) and healthy (HCs) subjects. The experiments involve 27 RMDDs (16 with acute depression – RMDD-A and 11 in a compensation phase – RMDD-C), 16 Ads, and 16 HCs. The ability to decode emotional expressions is assessed through an emotion recognition task based on short audio (without video), video (without audio), and audio/video clips. The results show that AD patients are significantly less accurate than HCs in decoding fear, anger, happiness, surprise, and sadness. RMDD-As with acute depression are significantly less accurate than HCs in decoding happiness, sadness, and surprise. Finally, no significant differences were found between HCs and RMDD-Cs in a compensation phase. The different communication channels and the types of emotion play a significant role in limiting the decoding accuracy.
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Drafts by Filomena Scibelli
Papers by Filomena Scibelli