Clinical Depression and Affect Recognition with EmoAudioNet

no code implementations • 1 Nov 2019 • Emna Rejaibi, Daoud Kadoch, Kamil Bentounes, Romain Alfred, Mohamed Daoudi, Abdenour Hadid, Alice Othmani

Automatic analysis of emotions and affects from speech is an inherently challenging problem with a broad range of applications in Human-Computer Interaction (HCI), health informatics, assistive technologies and multimedia retrieval.

Human-Computer Interaction Sound Audio and Speech Processing

MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech

no code implementations • 16 Sep 2019 • Emna Rejaibi, Ali Komaty, Fabrice Meriaudeau, Said Agrebi, Alice Othmani

The proposed approach outperforms the state-of-art approaches on the DAIC-WOZ database with an overall accuracy of 76. 27% and a root mean square error of 0. 4 in assessing depression, while a root mean square error of 0. 168 is achieved in predicting the depression severity levels.

Data Augmentation Transfer Learning