  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 SOTA models on the IEMOCAP
dataset. The experimental results show that these models are improved by 0.2\%
- 4.8\% and 1.5\% - 5.9\% in terms of WA and UA. In addition, an ablation study
demonstrates that the key advantage of PDDS is the reasonable data distribution
rather than a simple data augmentation.
