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Depression is a serious medical condition that is suffered by a large numberof people around the world. It significantly affects the way one feels, causinga persistent lowering of mood. In this paper, we propose a novelattention-based deep neural network which facilitates the fusion of variousmodalities. We use this network to regress the depression level. Acoustic, textand visual modalities have been used to train our proposed network. Variousexperiments have been carried out on the benchmark dataset, namely, DistressAnalysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, weempirically justify that the fusion of all three modalities helps in giving themost accurate estimation of depression level. Our proposed approach outperformsthe state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% onmean absolute error (MAE).

The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level