标题： 基于大型语言模型的助听器零样本非侵入性语音可懂度研究 显示英文标题

标题： A Study on Zero-Shot Non-Intrusive Speech Intelligibility for Hearing Aids Using Large Language Models

摘要： 这项工作专注于使用大型语言模型（LLMs）对助听器（HA）进行零样本非侵入式语音评估。 具体而言，我们引入了GPT-Whisper-HA，这是GPT-Whisper的一个扩展，GPT-Whisper是一个基于LLMs的零样本非侵入式语音评估模型。 GPT-Whisper-HA专为助听器的语音评估设计，结合了MSBG听力损失和NAL-R模拟，根据每个个体的听力图处理音频输入，两个自动语音识别（ASR）模块用于音频到文本的表示，以及GPT-4o来预测两个相应的分数，随后对分数进行平均以获得最终估计分数。 实验结果表明，GPT-Whisper-HA相比GPT-Whisper在相对均方根误差（RMSE）上提高了2.59%，证实了LLMs在预测助听器用户主观可懂度方面的潜力。 显示英文摘要

摘要： This work focuses on zero-shot non-intrusive speech assessment for hearing aids (HA) using large language models (LLMs). Specifically, we introduce GPT-Whisper-HA, an extension of GPT-Whisper, a zero-shot non-intrusive speech assessment model based on LLMs. GPT-Whisper-HA is designed for speech assessment for HA, incorporating MSBG hearing loss and NAL-R simulations to process audio input based on each individual's audiogram, two automatic speech recognition (ASR) modules for audio-to-text representation, and GPT-4o to predict two corresponding scores, followed by score averaging for the final estimated score. Experimental results indicate that GPT-Whisper-HA achieves a 2.59% relative root mean square error (RMSE) improvement over GPT-Whisper, confirming the potential of LLMs for zero-shot speech assessment in predicting subjective intelligibility for HA users.