Before revision:  Our model  outperforms the state-of-the-art methods for RECOLA and for DAIC-WOZ datasets and it reaches high accuracies of 89.30\%, 91.44\% and 73.25\% in predicting arousal, valence, and depression , respectively.
After revision:  Our model outperforms the state-of-the-art methods according to several evaluation metrics on RECOLA and on DAIC-WOZ datasets and it reaches high accuracies of 89.30\%, 91.44\% and 73.25\% in predicting arousal, valence, and depression , respectively.