Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition
This work addresses data imbalance issues for researchers and practitioners in speech emotion recognition, representing an incremental improvement.
The paper tackles the problem of imbalanced data distribution in speech emotion recognition by proposing a Gaussian-smoothed method to create a new training set, resulting in improvements of 0.2% to 5.9% in weighted and unweighted accuracy on the IEMOCAP dataset.
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.