An Investigation of Annotation Delay Compensation and Output-Associative Fusion for Multimodal Continuous Emotion Prediction

Continuous emotion dimension prediction has increased in popularity over the last few years, as the shift away from discrete classification based tasks has introduced more realism in emotion modeling. However, many questions remain including how best to combine information from several modalities (e.g. audio, video, etc). As part of the AV+EC 2015 Challenge, we investigate annotation delay compensation and propose a range of multimodal systems based on an output-associative fusion framework. The performance of the proposed systems are significantly higher than the challenge baseline, with the strongest performing system yielding 66.7% and 53.9% relative increases in prediction accuracy over the AV+EC 2015 test set arousal and valence baselines respectively. Results also demonstrate the importance of annotation delay compensation for continuous emotion analysis. Of particular interest was the output-associative based fusion framework, which performed very well in a number of significantly different configurations, highlighting that incorporating both affective dimensional dependencies and temporal information is a promising research direction for predicting emotion dimensions.