Naffah Ava, Pfeifer Valeria A, Mehl Matthias R
Department of Psychology, University of Arizona, Tucson, Arizona, USA.
Gerontology. 2025;71(5):417-424. doi: 10.1159/000545244. Epub 2025 Mar 13.
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.
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.
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.
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.
研究老年人的言语可以为衰老的认知、情感和社会方面提供重要见解。现有的语言分析工具通常需要将录音语音转录为逐字文本，这项任务历来由人工完成。然而，基于人工智能的语言处理的最新进展开启了用全自动语音转文本取代这项耗时且资源密集型任务的可能性。
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.ai——相对于人工校正转录本的准确性。基于238名老年人完成的两项言语任务，我们使用语言查询与字数统计（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对口语中常见的填充词（如“like”“well”）进行人工标记对分析有效性的影响程度。
基于人工智能的LIWC特征与从人工校正转录本中得出的LIWC特征显示出非常高的一致性（平均r = 0.98）。此外，除了填充词和网络用语类别外，对填充词进行人工标记并未影响所有LIWC特征的有效性。
这些发现支持Whisper和otter.ai是衰老研究中语言分析的有价值工具，并进一步证明使用先进的人工智能工具进行自动语音转文本已可用于心理学语言研究。
