Temporal-aware Modelling of Emotions and Mental States
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Exploration of the temporal dynamics of emotion attributes, and incorporate it in the prediction system.
Development of novel machine learning/deep learning frameworks to capture the temporal dependencies of emotions and predict time-varying emotion states. Some key techniques applied including but not limited to: filtering methods (i.e., Kalman filter), Hidden Markov Models, Long Short-Term Memory network (LSTM), Neural Ordinary Differential Equations and Stochastic Differential Equations etc.
Ambiguity Aware Speech Emotion Recognition
Investigation and modelling of the uncertainty of continuous time series labels in the application of emotion recognition that reflects the variations of human perceptions.
Development of comprehensive emotion representations with time-varying label distributions using probabilistic frameworks, such as Monte Carlo algorithms, stochastic process etc.
Multisensory Intelligence for Human Behaviour Modelling
Analysing relative contributions of audio and visual cues in emotion recognition and how cross-modal patterns evolve during dynamic interactions.
Analysing multimodal Large Language Models (LLMs)’ reasoning chains and their robustness to modality sabotage.
Design unified and holistic architectures for diverse affective and human-behaviour tasks, jointly trained with a ranges of co-occuring modalities (e.g., depression, stress or arousal)
Emotion Recognition in Ordinal Scale
Exploration of the relationships among different emotion representations among interval, ordinal and categorical scales, with specializion in ordinal scale for less ambiguous emotion labels.
Development of novel framework to convert emotion labels from interval to ordinal with the integration of rater individual information and emotion dynamics.
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