Before revision:  Our method outputs depression presence results as well as predicted severity score, culminating a state-of-the-art F1 score of 0.87, outperforming previous multi-modal studies. We also achieve the lowest RMSE compared with currently available text-based approaches. Further, by utilizing a per time step attention mechanism we analyse the sentences/words that contribute most in predicting the depressed state. Surprisingly, `unmeaningful' words/paralinguistic information such as `um' and `uh' are the indicators to our model when making a depression prediction. It is for the first time revealed that fillers in a conversation trigger a depression alert for a deep learning model .
After revision:  Our method outputs depression presence results as well as predicted severity score, culminating a state-of-the-art F1 score of 0.84 and MAE of 3.48 on the DAIC-WOZ development set .