Jiaxin Ye

TL;DR: A novel Emotion Decoupling aNd Alignment learning framework (EMO-DNA) for cross-corpus SER, a novel UDA method to learn emotion-relevant corpus-invariant features, and a dual-level emotion alignment that introduces an adaptive threshold pseudo-labeling to select confident target samples for class-level alignment.

TL;DR: A Capsule Network (CapsNet) and Transfer Learning based Mixed Task Net (CTL-MTNet) are proposed to deal with both the single-Corpus and cross-corpus SER tasks simultaneously in this paper, showing better performance in all cases compared to a number of state-of-the-art methods.

TL;DR: The proposed Capsule Network with Two-Way Attention Mechanism for the SER problem outperforms the widely-deployed neural network models on three typical SER data sets and the combination of the two ways contributes to the higher and more stable performance of TWACapsNet.

TL;DR: TIM-Net as mentioned in this paper employs temporal-aware blocks to learn temporal affective representation, then integrates complementary information from the past and the future to enrich contextual representations, and finally, fuses multiple time scale features for better adaptation to the emotional variation.