ArticleGerontology2025
Spoken Language Analysis in Aging Research: The Validity of AI-Generated Speech to Text Using OpenAI's Whisper.
Article in Gerontology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and Interpretation Study.JMIR medical informatics · 2025Article
- Autoscribe: An automated tool for creating transcribed TextGrids from audio-recorded conversations.Behavior research methods · 2025Article
- Fabla: A voice-based ecological assessment method for securely collecting spoken responses to researcher questions.Behavior research methods · 2025Article
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Corrections and comments
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Authors and funding
3 authors.
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Abstract
introductionStudying 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.
methodsThis 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.
resultsThe 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.
conclusionThese 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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Registered trials
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