标题： 使用预训练语言模型和大语言模型评估ESL语音的短语中断 显示英文标题

标题： Assessing Phrase Break of ESL Speech with Pre-trained Language Models and Large Language Models

摘要： 这项工作介绍了使用预训练语言模型（PLMs）和大语言模型（LLMs）评估英语学习者口语中短语断点的方法。 有两个任务：对一段语音片段进行整体的短语断点评估，以及对每一个可能的短语断点位置进行细粒度评估。 为了利用NLP模型，首先将语音输入与文本进行强制对齐，然后将其预处理为包含单词和短语断点信息的标记序列。 为了利用PLMs，我们提出了一个带有处理后标记的预训练和微调流程。 该过程包括使用替换断点标记检测模块进行预训练，以及使用文本分类和序列标注进行微调。 为了使用LLMs，我们设计了针对ChatGPT的提示。 实验表明，使用PLMs后，对标注训练数据的依赖大大减少，性能得到了提升。 同时，我们验证了著名的LLM ChatGPT在这一领域具有进一步发展的潜力。 显示英文摘要

摘要： This work introduces approaches to assessing phrase breaks in ESL learners' speech using pre-trained language models (PLMs) and large language models (LLMs). There are two tasks: overall assessment of phrase break for a speech clip and fine-grained assessment of every possible phrase break position. To leverage NLP models, speech input is first force-aligned with texts, and then pre-processed into a token sequence, including words and phrase break information. To utilize PLMs, we propose a pre-training and fine-tuning pipeline with the processed tokens. This process includes pre-training with a replaced break token detection module and fine-tuning with text classification and sequence labeling. To employ LLMs, we design prompts for ChatGPT. The experiments show that with the PLMs, the dependence on labeled training data has been greatly reduced, and the performance has improved. Meanwhile, we verify that ChatGPT, a renowned LLM, has potential for further advancement in this area.