  Speech emotion recognition is a challenging classification task with natural
emotional speech, especially when the distribution of emotion types is
imbalanced in the training and test data. In this case, it is more difficult
for a model to learn to separate minority classes, resulting in those sometimes
being ignored or frequently misclassified. Previous work has utilised class
weighted loss for training, but problems remain as it sometimes causes
over-fitting for minor classes or under-fitting for major classes. This paper
presents the system developed by a multi-site team for the participation in the
Odyssey 2024 Emotion Recognition Challenge Track-1. The challenge data has the
aforementioned properties and therefore the presented systems aimed to tackle
these issues, by introducing focal loss in optimisation when applying class
weighted loss. Specifically, the focal loss is further weighted by prior-based
class weights. Experimental results show that combining these two approaches
brings better overall performance, by sacrificing performance on major classes.
The system further employs a majority voting strategy to combine the outputs of
an ensemble of 7 models. The models are trained independently, using different
acoustic features and loss functions - with the aim to have different
properties for different data. Hence these models show different performance
preferences on major classes and minor classes. The ensemble system output
obtained the best performance in the challenge, ranking top-1 among 68
submissions. It also outperformed all single models in our set. On the Odyssey
2024 Emotion Recognition Challenge Task-1 data the system obtained a Macro-F1
score of 35.69% and an accuracy of 37.32%.
