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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 speechclip and fine-grained assessment of every possible phrase break position. Toleverage NLP models, speech input is first force-aligned with texts, and thenpre-processed into a token sequence, including words and phrase breakinformation. To utilize PLMs, we propose a pre-training and fine-tuningpipeline with the processed tokens. This process includes pre-training with areplaced break token detection module and fine-tuning with text classificationand sequence labeling. To employ LLMs, we design prompts for ChatGPT. Theexperiments show that with the PLMs, the dependence on labeled training datahas been greatly reduced, and the performance has improved. Meanwhile, weverify that ChatGPT, a renowned LLM, has potential for further advancement inthis area.

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