Language-agnostic, Automated Assessment of Listeners' Speech Recall Using Large Language Models.

IF 3 2区 医学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY
Trends in Hearing Pub Date : 2025-01-01 Epub Date: 2025-05-30 DOI:10.1177/23312165251347131
Björn Herrmann
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引用次数: 0

Abstract

Speech-comprehension difficulties are common among older people. Standard speech tests do not fully capture such difficulties because the tests poorly resemble the context-rich, story-like nature of ongoing conversation and are typically available only in a country's dominant/official language (e.g., English), leading to inaccurate scores for native speakers of other languages. Assessments for naturalistic, story speech in multiple languages require accurate, time-efficient scoring. The current research leverages modern large language models (LLMs) in native English speakers and native speakers of 10 other languages to automate the generation of high-quality, spoken stories and scoring of speech recall in different languages. Participants listened to and freely recalled short stories (in quiet/clear and in babble noise) in their native language. Large language model text-embeddings and LLM prompt engineering with semantic similarity analyses to score speech recall revealed sensitivity to known effects of temporal order, primacy/recency, and background noise, and high similarity of recall scores across languages. The work overcomes limitations associated with simple speech materials and testing of closed native-speaker groups because recall data of varying length and details can be mapped across languages with high accuracy. The full automation of speech generation and recall scoring provides an important step toward comprehension assessments of naturalistic speech with clinical applicability.

语言不可知论,使用大型语言模型对听者语音回忆的自动评估。
语言理解困难在老年人中很常见。标准的语言测试不能完全捕捉到这些困难,因为这些测试与正在进行的对话的上下文丰富、故事般的性质很不相似,而且通常只适用于一个国家的主导语言/官方语言(例如英语),导致以其他语言为母语的人得分不准确。对多种语言的自然主义、故事演讲的评估需要准确、省时的评分。目前的研究利用现代大型语言模型(llm),以英语为母语和其他10种语言为母语的人,自动生成高质量的口语故事,并对不同语言的语音回忆进行评分。参与者用母语听并自由回忆短篇故事(安静/清晰,咿呀学语)。大型语言模型文本嵌入和LLM提示工程利用语义相似度分析对语音回忆进行评分,揭示了对时间顺序、首因/近因和背景噪声的已知影响的敏感性,以及不同语言之间回忆分数的高相似性。这项工作克服了简单的语音材料和封闭的母语人群测试的局限性,因为不同长度和细节的回忆数据可以高精度地映射到不同的语言中。语音生成和回忆评分的完全自动化为具有临床适用性的自然语音理解评估提供了重要的一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Trends in Hearing
Trends in Hearing AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGYOTORH-OTORHINOLARYNGOLOGY
CiteScore
4.50
自引率
11.10%
发文量
44
审稿时长
12 weeks
期刊介绍: Trends in Hearing is an open access journal completely dedicated to publishing original research and reviews focusing on human hearing, hearing loss, hearing aids, auditory implants, and aural rehabilitation. Under its former name, Trends in Amplification, the journal established itself as a forum for concise explorations of all areas of translational hearing research by leaders in the field. Trends in Hearing has now expanded its focus to include original research articles, with the goal of becoming the premier venue for research related to human hearing and hearing loss.
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