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.

本文提出了一种新型的失误函数 —— 得分平衡损失函数，侧重于解决自动发音评估中存在的数据分布不均问题，通过引入重新加权方法，使模型收到最小类的预测分数较高的成本支出，从而提升模型在少量分数预测问题上的性能。在 speechocean762 数据集上验证了本方法的有效性。