ArticleBehavior research methods2021
Using fuzzy string matching for automated assessment of listener transcripts in speech intelligibility studies.
Article in Behavior research methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Assessing the Effectiveness of Automatic Speech Recognition Technology in Emergency Medicine Settings: a Comparative Study of Four AI-Powered Engines.Journal of healthcare informatics research · 2025Article
- Making manual scoring of typed transcripts a thing of the past: a commentary on Herrmann (2025).Speech, language and hearing (London, England) · 2025Article
- Older adults' recognition of medical terminology in hospital noise.Cognitive research: principles and implications · 2024Article
- A search-based geographic metadata curation pipeline to refine sequencing institution information and support public health.Frontiers in public health · 2023Article
- Adaptation to Social-Linguistic Associations in Audio-Visual Speech.Brain sciences · 2022Article
- Comparison of Machine-Learning Algorithms for the Prediction of Current Procedural Terminology (CPT) Codes from Pathology Reports.Journal of pathology informatics · 2022Article
- Visualization of Speech Perception AnalysisFrontiers in neurology · 2021Article
- Language-agnostic, Automated Assessment of Listeners' Speech Recall Using Large Language Models.Trends in hearingArticle
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1 author.
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Abstract
Many studies of speech perception assess the intelligibility of spoken sentence stimuli by means of transcription tasks ('type out what you hear'). The intelligibility of a given stimulus is then often expressed in terms of percentage of words correctly reported from the target sentence. Yet scoring the participants' raw responses for words correctly identified from the target sentence is a time-consuming task, and hence resource-intensive. Moreover, there is no consensus among speech scientists about what specific protocol to use for the human scoring, limiting the reliability of human scores. The present paper evaluates various forms of fuzzy string matching between participants' responses and target sentences, as automated metrics of listener transcript accuracy. We demonstrate that one particular metric, the token sort ratio, is a consistent, highly efficient, and accurate metric for automated assessment of listener transcripts, as evidenced by high correlations with human-generated scores (best correlation: r = 0.940) and a strong relationship to acoustic markers of speech intelligibility. Thus, fuzzy string matching provides a practical tool for assessment of listener transcript accuracy in large-scale speech intelligibility studies. See https://tokensortratio.netlify.app for an online implementation.
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