Spoken Language Analysis in Aging Research: The Validity of AI-Generated Speech to Text Using OpenAI's Whisper.

IF 3 3区医学 Q3 GERIATRICS & GERONTOLOGY

Gerontology Pub Date : 2025-01-01 Epub Date: 2025-03-13 DOI:10.1159/000545244

Ava Naffah, Valeria A Pfeifer, Matthias R Mehl

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引用次数: 0

Abstract

Introduction: Studying what older adults say can provide important insights into cognitive, affective, and social aspects of aging. Available language analysis tools generally require audio-recorded speech to be transcribed into verbatim text, a task that has historically been performed by humans. However, recent advances in AI-based language processing open up the possibility of replacing this time- and resource-intensive task with fully automatic speech to text.

Methods: This study evaluates the accuracy of two common automatic speech-to-text tools - OpenAI's Whisper and otter.ai - relative to human-corrected transcripts. Based on two speech tasks completed by 238 older adults, we used the Linguistic Inquiry and Word Count (LIWC) to compare language features of text generated by each transcription method. The study further assessed the degree to which manual tagging of filler words (e.g., "like," "well") common in spoken language impacts the validity of the analysis.

Results: The AI-based LIWC features evidenced very high convergence with the LIWC features derived from the human-corrected transcripts (average r = 0.98). Further, the manual tagging of filler words did not impact the validity for all LIWC features except the categories filler words and netspeak.

Conclusion: These findings support that Whisper and otter.ai are valuable tools for language analysis in aging research and provide further evidence that automatic speech to text with state-of-the art AI tools is ready for psychological language research.

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老龄化研究中的口语分析：使用OpenAI的Whisper对人工智能生成的语音文本的有效性。

导读：研究老年人所说的话可以对衰老的认知、情感和社会方面提供重要的见解。可用的语言分析工具通常需要将录音语音逐字转录成文本，这是一项历史上由人类执行的任务。然而，基于人工智能的语言处理的最新进展开辟了用全自动语音到文本取代这种时间和资源密集型任务的可能性。方法：本研究评估了两种常见的自动语音转文本工具——OpenAI的Whisper和otter的准确性。Ai -相对于人工校正的文本。基于238名老年人完成的两项语音任务，我们使用语言查询和单词计数（LIWC）来比较每种转录方法生成的文本的语言特征。该研究进一步评估了口语中常见的人工标注填充词（如“like”、“well”）对分析有效性的影响程度。结果：基于人工智能的LIWC特征与来自人工校正转录本的LIWC特征具有很高的收敛性（平均r = 0.98）。此外，人工标注填充词对除填充词和网络语言类别外的所有LIWC特征的有效性没有影响。结论：这些发现支持了Whisper和水獭。人工智能是老龄化研究中语言分析的宝贵工具，并进一步证明了使用最先进的人工智能工具进行自动语音转文本已为心理语言研究做好了准备。

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

Gerontology 医学-老年医学

CiteScore

6.00

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： In view of the ever-increasing fraction of elderly people, understanding the mechanisms of aging and age-related diseases has become a matter of urgent necessity. ''Gerontology'', the oldest journal in the field, responds to this need by drawing topical contributions from multiple disciplines to support the fundamental goals of extending active life and enhancing its quality. The range of papers is classified into four sections. In the Clinical Section, the aetiology, pathogenesis, prevention and treatment of agerelated diseases are discussed from a gerontological rather than a geriatric viewpoint. The Experimental Section contains up-to-date contributions from basic gerontological research. Papers dealing with behavioural development and related topics are placed in the Behavioural Science Section. Basic aspects of regeneration in different experimental biological systems as well as in the context of medical applications are dealt with in a special section that also contains information on technological advances for the elderly. Providing a primary source of high-quality papers covering all aspects of aging in humans and animals, ''Gerontology'' serves as an ideal information tool for all readers interested in the topic of aging from a broad perspective.