In automated pronunciation assessment, recent emphasis progressively lies on
evaluating multiple aspects to provide enriched feedback. However, acquiring
multi-aspect-score labeled data for non-native language learners' speech poses
challenges; moreover, it often leads to score-imbalanced distributions. In this
paper, we propose two Acoustic Feature Mixup strategies, linearly and
non-linearly interpolating with the in-batch averaged feature, to address data
scarcity and score-label imbalances. Primarily using goodness-of-pronunciation
as an acoustic feature, we tailor mixup designs to suit pronunciation
assessment. Further, we integrate fine-grained error-rate features by comparing
speech recognition results with the original answer phonemes, giving direct
hints for mispronunciation. Effective mixing of the acoustic features notably
enhances overall scoring performances on the speechocean762 dataset, and
detailed analysis highlights our potential to predict unseen distortions.

本文提出了两种声学特征混合策略，线性和非线性插值与批次平均特征相结合，以解决非母语学习者语音数据稀缺和得分标签不平衡的问题。通过将发音准确性作为声学特征，我们改进了设计以适应发音评估，同时结合细粒度的错误率特征，通过将语音识别结果与原始答案音素进行比较，直接提供发音错误的提示。声学特征的有效混合显著提高了 speechocean762 数据集上的整体评分性能，详细分析突显了我们预测未见扭曲的潜力。