Automatic Pronunciation Assessment (APA) is vital for computer-assisted
language learning. Prior methods rely on annotated speech-text data to train
Automatic Speech Recognition (ASR) models or speech-score data to train
regression models. In this work, we propose a novel zero-shot APA method based
on the pre-trained acoustic model, HuBERT. Our method involves encoding speech
input and corrupting them via a masking module. We then employ the Transformer
encoder and apply k-means clustering to obtain token sequences. Finally, a
scoring module is designed to measure the number of wrongly recovered tokens.
Experimental results on speechocean762 demonstrate that the proposed method
achieves comparable performance to supervised regression baselines and
outperforms non-regression baselines in terms of Pearson Correlation
Coefficient (PCC). Additionally, we analyze how masking strategies affect the
performance of APA.

提出基于 HuBERT 的零样本自动发音评估方法，使用遮罩模块破坏语音输入获取令牌序列并使用得分模块测量错误恢复的令牌数量，在 speechocean762 上得到了可比较于监督回归基线且优于非回归基线的表现，同时分析了遮罩策略对自动发音评估性能的影响。

零样本自动发音评估

Zero-Shot Automatic Pronunciation Assessment

Over the last two decades, technology use in language learning and teaching
has significantly advanced and is now referred to as Computer-Assisted Language
Learning (CALL). Recently, the integration of Artificial Intelligence (AI) into
CALL has brought about a significant shift in the traditional approach to
language education both inside and outside the classroom. In line with this
book's scope, I explore the advantages and disadvantages of AI-mediated
communication in language education. I begin with a brief review of AI in
education. I then introduce the ICALL and give a critical appraisal of the
potential of AI-powered automatic speech recognition (ASR), Machine Translation
(MT), Intelligent Tutoring Systems (ITSs), AI-powered chatbots, and Extended
Reality (XR). In conclusion, I argue that it is crucial for language teachers
to engage in CALL teacher education and professional development to keep up
with the ever-evolving technology landscape and improve their teaching
effectiveness.

本文主要探讨了计算机辅助语言学习 (CALL) 的新型变革 —— 人工智能 (AI) 和交互式计算机辅助语言学习 (ICALL)，并对 ASR、MT、ITS、AI 聊天机器人和 XR 等技术的发展与应用进行了分析和评估，认为语言教师需要积极学习与应用 CALL 技术，提高教育教学的效率。

人工智能介导的通讯的重要评审

Critical Appraisal of Artificial Intelligence-Mediated Communication

Determination of mispronunciations and ensuring feedback to users are
maintained by computer-assisted language learning (CALL) systems. In this work,
we introduce an ensemble model that defines the mispronunciation of Arabic
phonemes and assists learning of Arabic, effectively. To the best of our
knowledge, this is the very first attempt to determine the mispronunciations of
Arabic phonemes employing ensemble learning techniques and conventional machine
learning models, comprehensively. In order to observe the effect of feature
extraction techniques, mel-frequency cepstrum coefficients (MFCC), and Mel
spectrogram are blended with each learning algorithm. To show the success of
proposed model, 29 letters in the Arabic phonemes, 8 of which are hafiz, are
voiced by a total of 11 different person. The amount of data set has been
enhanced employing the methods of adding noise, time shifting, time stretching,
pitch shifting. Extensive experiment results demonstrate that the utilization
of voting classifier as an ensemble algorithm with Mel spectrogram feature
extraction technique exhibits remarkable classification result with 95.9% of
accuracy.

本文介绍了一种利用集成学习模型识别阿拉伯语音素发音错误并帮助学习阿拉伯语的方法，并使用 Mel 频谱图特征提取技术将投票分类器作为集成算法，取得了 95.9% 的分类准确率。