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。