In recent years, self-supervised learning (SSL) models have produced
promising results in a variety of speech-processing tasks, especially in
contexts of data scarcity. In this paper, we study the use of SSL models for
the task of mispronunciation detection for second language learners. We compare
two downstream approaches: 1) training the model for phone recognition (PR)
using native English data, and 2) training a model directly for the target task
using non-native English data. We compare the performance of these two
approaches for various SSL representations as well as a representation
extracted from a traditional DNN-based speech recognition model. We evaluate
the models on L2Arctic and EpaDB, two datasets of non-native speech annotated
with pronunciation labels at the phone level. Overall, we find that using a
downstream model trained for the target task gives the best performance and
that most upstream models perform similarly for the task.

本文研究了自我监督学习（SSL）模型在第二语言学习者的发音错误检测任务中的应用，比较了使用母语英语数据训练模型的语音识别（PR）方法和直接使用非母语英语数据训练模型的目标任务方法，通过对 L2Arctic 和 EpaDB 两个非母语语音数据集上的评估发现，直接训练用于目标任务的下游模型表现最佳，而大多数上游模型在该任务中表现相似。

利用自监督语音表征进行发音错误检测

Mispronunciation detection using self-supervised speech representations

This paper addresses the task of readability assessment for the texts aimed
at second language (L2) learners. One of the major challenges in this task is
the lack of significantly sized level-annotated data. For the present work, we
collected a dataset of CEFR-graded texts tailored for learners of English as an
L2 and investigated text readability assessment for both native and L2
learners. We applied a generalization method to adapt models trained on larger
native corpora to estimate text readability for learners, and explored domain
adaptation and self-learning techniques to make use of the native data to
improve system performance on the limited L2 data. In our experiments, the best
performing model for readability on learner texts achieves an accuracy of 0.797
and PCC of $0.938$.

该研究针对非英语母语者阅读理解评估的不足，提出了一种利用一般化方法和自学习技术，借助母语数据提高 L2 数据系统表现的模型，并在学习者文本的可读性评估方面取得了精度为 0.797 和相关系数为 0.938 的结果。

二语学习者文本可读性评估

Text Readability Assessment for Second Language Learners

Grammatical error correction (GEC) is the task of detecting and correcting
grammatical errors in texts written by second language learners. The
statistical machine translation (SMT) approach to GEC, in which sentences
written by second language learners are translated to grammatically correct
sentences, has achieved state-of-the-art accuracy. However, the SMT approach is
unable to utilize global context. In this paper, we propose a novel approach to
improve the accuracy of GEC, by exploiting the n-best hypotheses generated by
an SMT approach. Specifically, we build a classifier to score the edits in the
n-best hypotheses. The classifier can be used to select appropriate edits or
re-rank the n-best hypotheses. We apply these methods to a state-of-the-art GEC
system that uses the SMT approach. Our experiments show that our methods
achieve statistically significant improvements in accuracy over the best
published results on a benchmark test dataset on GEC.

本文提出了一种利用 SMT 方法生成的 n 种翻译假设来提高 GEC 准确率的新方法，即利用分类器评分来选择适当的编辑或对 n 种翻译假设进行重新排序，并将这些方法应用于使用 SMT 方法的最新 GEC 系统中，实验结果表明，我们的方法在 GEC 基准测试数据集上的准确性比已发表的最佳结果有显着提高。