Multitask Representation Learning for Multimodal Estimation of Depression Level
We propose a novel multitask learning attention -based deep neural network model, which facilitates the fusion of various modalities. In particular, we use this network to both regress and classify the level of depression. Acoustic, textual, and visual modalities have been used to train our proposed...
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| Veröffentlicht in: | IEEE intelligent systems 2019-09, Vol.34 (5), p.45-52 |
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| container_title | IEEE intelligent systems |
| container_volume | 34 |
| creator | Qureshi, Syed Arbaaz Saha, Sriparna Hasanuzzaman, Mohammed Dias, Gael Cambria, Erik |
| description | We propose a novel multitask learning attention -based deep neural network model, which facilitates the fusion of various modalities. In particular, we use this network to both regress and classify the level of depression. Acoustic, textual, and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus -a Wizard of Oz. From the results, we empirically justify that a) multitask learning networks cotrained over regression and classification have better performance compared to single -task networks, and b) the fusion of all the modalities helps in giving the most accurate estimation of depression with respect to regression. |
| doi_str_mv | 10.1109/MIS.2019.2925204 |
| format | Article |
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| language | eng |
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| subjects | Affective computing Depression Estimation Learning systems Medical conditions |
| title | Multitask Representation Learning for Multimodal Estimation of Depression Level |
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