Automatic pronunciation assessment (APA) manages to quantify the
pronunciation proficiency of a second language (L2) learner in a language.
Prevailing approaches to APA normally leverage neural models trained with a
regression loss function, such as the mean-squared error (MSE) loss, for
proficiency level prediction. Despite most regression models can effectively
capture the ordinality of proficiency levels in the feature space, they are
confronted with a primary obstacle that different phoneme categories with the
same proficiency level are inevitably forced to be close to each other,
retaining less phoneme-discriminative information. On account of this, we
devise a phonemic contrast ordinal (PCO) loss for training regression-based APA
models, which aims to preserve better phonemic distinctions between phoneme
categories meanwhile considering ordinal relationships of the regression target
output. Specifically, we introduce a phoneme-distinct regularizer into the MSE
loss, which encourages feature representations of different phoneme categories
to be far apart while simultaneously pulling closer the representations
belonging to the same phoneme category by means of weighted distances. An
extensive set of experiments carried out on the speechocean762 benchmark
dataset suggest the feasibility and effectiveness of our model in relation to
some existing state-of-the-art models.

我们提出了一种训练基于回归的自动发音评估模型的方法，通过引入音素 - 对比次序（PCO）损失函数，从而更好地保留了音素类别之间的区分度和回归目标输出的顺序关系，并在一些现有的最先进模型上进行了可行性和有效性的实验证明。