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。

自动发音评估的新型损失函数：保留音素区分的序数回归

Preserving Phonemic Distinctions for Ordinal Regression: A Novel Loss  Function for Automatic Pronunciation Assessment

Although two-stage object detectors have continuously advanced the
state-of-the-art performance in recent years, the training process itself is
far from crystal. In this work, we first point out the inconsistency problem
between the fixed network settings and the dynamic training procedure, which
greatly affects the performance. For example, the fixed label assignment
strategy and regression loss function cannot fit the distribution change of
proposals and thus are harmful to training high quality detectors.
Consequently, we propose Dynamic R-CNN to adjust the label assignment criteria
(IoU threshold) and the shape of regression loss function (parameters of
SmoothL1 Loss) automatically based on the statistics of proposals during
training. This dynamic design makes better use of the training samples and
pushes the detector to fit more high quality samples. Specifically, our method
improves upon ResNet-50-FPN baseline with 1.9% AP and 5.5% AP$_{90}$ on the MS
COCO dataset with no extra overhead. Codes and models are available at
this https URL

本文提出了 Dynamic R-CNN 以调整标签分配标准和回归损失函数的形状，这使得训练样本得到更好的利用，并使检测器适应更多的高质量样本。在 MS COCO 数据集上，我们的方法基于 ResNet-50-FPN 基线改进了 1.9% AP 和 5.5% AP$_{90}$