使用大型语言模型的自动化问答式噪声中言语听力测试

文献信息

中文摘要

一种无需人工监督即可实施的对话式噪声中言语测试，有望减少前往诊所的需求。本研究旨在创建并评估一种新型的基于问答的噪声中言语测试，称为“CHAT”，其中刺激使用文本转语音（TTS）合成，应答使用自动语音识别（ASR）捕获，并使用大型语言模型（LLM）评估应答。比较了三种测试：新开发的CHAT、自适应句子列表（ASL）和四选一听觉特征测试（FAAF）。CHAT使用合成语音，而ASL和FAAF默认使用人工录制的语音。使用这三种测试测量了20名听力正常参与者和20名轻度至中度听力损失参与者的言语接受阈（SRT）。通过受试者工作特征（ROC）分析以及三种言语测试中SRT与平均纯音测听（PTA）之间的相关性来评估效度。通过使用Bland-Altman分析比较CHAT和ASL测试重复运行所达到的SRT之间的一致性界限来评估信度。结果显示，CHAT和ASL重复运行之间的一致性界限分别为±3.6 dB和±3.1 dB。CHAT的ROC曲线下面积为0.97，ASL为0.95，FAAF为0.89。SRT与PTA结果之间的相关性，CHAT为0.86，ASL为0.82，FAAF为0.79。这些发现表明，CHAT表现出与人工录制的ASL和FAAF相当的信度和效度，支持使用合成语音进行噪声中言语测试。总体而言，结果表明AI工具可以帮助创建和实施对话式噪声中言语测试。

英文摘要

A conversational speech-in-noise test administered without human supervision is desirable to reduce the need to attend the clinic. This study aimed to create and evaluate a novel question-and-answer-based speech-in-noise test called 'CHAT', where stimuli were synthesised using text-to-speech (TTS), responses captured using automatic speech recognition (ASR), and responses evaluated using a large language model (LLM). Three tests were compared: the newly developed CHAT, the Adaptive Sentence List (ASL) and the Four Alternative Auditory Feature Test (FAAF). CHAT used synthetic speech while ASL and FAAF by default used human-recorded speech. Speech reception thresholds (SRT) of 20 participants with normal hearing and 20 with mild to moderate hearing loss were measured using these three tests. Validity was evaluated using receiver operating characteristic (ROC) analysis and correlations between SRTs and average pure-tone audiometry (PTA), across the three speech tests. Reliability was evaluated by comparing limits-of-agreement between SRTs achieved by repeated runs of CHAT and ASL tests using Bland-Altman analysis. Results revealed limits-of-agreement between repeated runs of CHAT and ASL were ±3.6 dB and ±3.1 dB, respectively. The area under the ROC curve was 0.97 for CHAT, 0.95 for ASL and 0.89 for FAAF. The correlation between SRT and PTA results was 0.86 for CHAT, 0.82 for ASL and 0.79 for FAAF. These findings indicate that CHAT demonstrates reliability and validity comparable to human-recorded ASL and FAAF, supporting the use of synthetic speech for speech-in-noise testing. Overall, the results suggest that AI tools can help create and administer conversational speech-in-noise tests.