Pairwise-Emotion Data Distribution Smoothing for Emotion Recognition

In speech emotion recognition tasks, models learn emotional representations from datasets. We find the data distribution in the IEMOCAP dataset is very imbalanced, which may harm models to learn a better representation. To address this issue, we propose a novel Pairwise-emotion Data Distribution Smoothing (PDDS) method. PDDS considers that the distribution of emotional data should be smooth in reality, then applies Gaussian smoothing to emotion-pairs for constructing a new training set with a smoother distribution. The required new data are complemented using the mixup augmentation. As PDDS is model and modality agnostic, it is evaluated with three state-of-the-art models on two benchmark datasets. The experimental results show that these models are improved by 0.2% \(\sim \) 4.8% and 0.1% \(\sim \) 5.9% in terms of weighted accuracy and unweighted accuracy. In addition, an ablation study demonstrates that the key advantage of PDDS is the reasonable data distribution rather than a simple data augmentation.