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Hassan Hayat's Projects

interpretable-cnn-for-big-five-personality-traits-using-audio-data icon interpretable-cnn-for-big-five-personality-traits-using-audio-data

We developed an interpretable CNN for big five personality traits using human speech data. This project discovers the different frequency patterns of a human voice with respect to each five personality traits. This project will help us to understand the apparent personality of a human using his/her voice.

modeling-subjective-affect-annotations-with-multi-task-learning icon modeling-subjective-affect-annotations-with-multi-task-learning

We compare two generic Deep Learning architectures: a Single-Task (ST) architecture and a Multi-Task (MT) architecture. While the ST architecture models a single emotional perception each time, the MT architecture jointly models every single emotional and aggregated emotional perception at once.

predicting-the-subjective-responses-emotion-in-dialogues-with-multi-task-learning icon predicting-the-subjective-responses-emotion-in-dialogues-with-multi-task-learning

Anticipating the subjective emotional responses of the user is an interesting capacity for automatic dialogue systems. In this work, given a piece of a dialog, we addressed the problem of predicting the subjective emotional response of the upcoming utterances (i.e. the emo- tion that will be expressed by the next speaker when the speaker talks).

recognizing-emotions-evoked-by-movies-usingmultitask-learning icon recognizing-emotions-evoked-by-movies-usingmultitask-learning

We model the emotions evoked by videos in a different manner: instead of modeling the aggregated value we jointly model the emotions experienced by each viewer and the aggregated value using a multi-task learning approach. Concretely, we proposed two deep learning architectures: Single-Task (ST) architecture and Multi-Task (MT) architecture.

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