Hassan Hayat's Projects
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.
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.
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).
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.