Pronunciation assessment and its application in computer-aided pronunciation
training (CAPT) have seen impressive progress in recent years. With the rapid
growth in language processing and deep learning over the past few years, there
is a need for an updated review. In this paper, we review methods employed in
pronunciation assessment for both phonemic and prosodic. We categorize the main
challenges observed in prominent research trends, and highlight existing
limitations, and available resources. This is followed by a discussion of the
remaining challenges and possible directions for future work.

发音评估和其在计算机辅助发音训练（CAPT）中的应用近年来取得了令人瞩目的进展。本文回顾了发音评估方法在音位和韵律方面的应用，分类了主要的研究趋势中观察到的挑战，并强调了现有的限制和可用资源。接着讨论了剩余的挑战和未来工作的可能方向。

自动发音评估 - 综述

Automatic Pronunciation Assessment -- A Review

Textless self-supervised speech models have grown in capabilities in recent
years, but the nature of the linguistic information they encode has not yet
been thoroughly examined. We evaluate the extent to which these models' learned
representations align with basic representational distinctions made by humans,
focusing on a set of phonetic (low-level) and phonemic (more abstract)
contrasts instantiated in word-initial stops. We find that robust
representations of both phonetic and phonemic distinctions emerge in early
layers of these models' architectures, and are preserved in the principal
components of deeper layer representations. Our analyses suggest two sources
for this success: some can only be explained by the optimization of the models
on speech data, while some can be attributed to these models' high-dimensional
architectures. Our findings show that speech-trained HuBERT derives a low-noise
and low-dimensional subspace corresponding to abstract phonological
distinctions.

本文评估了自我监督语音模型的学习表示与人类语音的差异，结果表明这些模型在语音数据的优化和高维架构的帮助下成功地捕捉了语音的基本音素和音位特征，尤其是 speech-trained HuBERT 模型实现了抽象音位差异的低噪声和低维子空间。