Score-balanced Loss for Multi-aspect Pronunciation Assessment
This addresses data imbalance in pronunciation assessment for language learners, but it is incremental as it builds on existing re-weighting approaches.
The paper tackles the problem of imbalanced score labels in multi-aspect pronunciation assessment by proposing a score-balanced loss function, which improves results on uneven aspects in the speechocean762 dataset.
With rapid technological growth, automatic pronunciation assessment has transitioned toward systems that evaluate pronunciation in various aspects, such as fluency and stress. However, despite the highly imbalanced score labels within each aspect, existing studies have rarely tackled the data imbalance problem. In this paper, we suggest a novel loss function, score-balanced loss, to address the problem caused by uneven data, such as bias toward the majority scores. As a re-weighting approach, we assign higher costs when the predicted score is of the minority class, thus, guiding the model to gain positive feedback for sparse score prediction. Specifically, we design two weighting factors by leveraging the concept of an effective number of samples and using the ranks of scores. We evaluate our method on the speechocean762 dataset, which has noticeably imbalanced scores for several aspects. Improved results particularly on such uneven aspects prove the effectiveness of our method.