  Due to its ability to accurately predict emotional state using multimodal
features, audiovisual emotion recognition has recently gained more interest
from researchers. This paper proposes two methods to predict emotional
attributes from audio and visual data using a multitask learning and a fusion
strategy. First, multitask learning is employed by adjusting three parameters
for each attribute to improve the recognition rate. Second, a multistage fusion
is proposed to combine results from various modalities' final prediction. Our
approach used multitask learning, employed at unimodal and early fusion
methods, shows improvement over single-task learning with an average CCC score
of 0.431 compared to 0.297. A multistage method, employed at the late fusion
approach, significantly improved the agreement score between true and predicted
values on the development set of data (from [0.537, 0.565, 0.083] to [0.68,
0.656, 0.443]) for arousal, valence, and liking.
