SetPeER: Set-Based Personalized Emotion Recognition With Weak Supervision

Individual variability of expressive behaviors is a major challenge for emotion recognition systems. Personalized emotion recognition strives to adapt machine learning models to individual behaviors, thereby enhancing emotion recognition performance and overcoming the limitations of generalized emotion recognition systems. However, existing datasets for audiovisual emotion recognition either have a very low number of data points per speaker or include a limited number of speakers. The scarcity of data significantly limits the development and assessment of personalized models, hindering their ability to effectively learn and adapt to individual expressive styles. This paper introduces EmoCeleb: a large-scale, weakly labeled emotion dataset generated via cross-modal labeling. EmoCeleb comprises over 150 hours of audiovisual content from approximately 1,500 speakers, with a median of 50 utterances per speaker. This rich dataset provides a rich resource for developing and benchmarking personalized emotion recognition methods, including those requiring substantial data per individual, such as set learning approaches. We also propose SetPeER: a novel personalized emotion recognition architecture employing set learning. SetPeER effectively captures individual expressive styles by learning representative speaker features from limited data, achieving strong performance with as few as eight utterances per speaker. By leveraging set learning, SetPeER overcomes the limitations of previous approaches that struggle to learn effectively from limited data per individual. Through extensive experiments on EmoCeleb and established benchmarks, i.e, MSP-Podcast and MSP-Improv, we demonstrate the effectiveness of our dataset and the superior performance of SetPeER compared to existing methods for emotion recognition. Our work paves the way for more robust and accurate personalized emotion recognition systems.