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  Using fuzzy string matching for automated assessment of listener transcripts in speech intelligibility studies

Bosker, H. R. (in press). Using fuzzy string matching for automated assessment of listener transcripts in speech intelligibility studies. Behavior Research Methods.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0007-AAAC-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0007-AAAD-7
Genre: Journal Article

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Bosker 2021 BRM Fuzzy Matching.pdf (Preprint), 665KB
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Bosker 2021 BRM Fuzzy Matching.pdf
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 Creators:
Bosker, Hans R.1, 2, Author              
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1Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society, ou_792545              
2Donders Institute for Brain, Cognition and Behaviour, External Organizations, ou_55236              

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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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Language(s): eng - English
 Dates: 2021-01
 Publication Status: Accepted / In Press
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 Rev. Type: Peer
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Title: Behavior Research Methods
Source Genre: Journal
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 1554-3528
CoNE: https://pure.mpg.de/cone/journals/resource/1554-3528