Making manual scoring of typed transcripts a thing of the past: a commentary on Herrmann (2025).
Coding the accuracy of typed transcripts from experiments testing speech intelligibility is an arduous endeavour. A recent study in this journal [Herrmann, B. 2025. Leveraging natural language processing models to automate speech-intelligibility scoring. Speech, Language and Hearing, 28(1)] presents...
| Publicado en: | Speech, Language & Hearing Vol. 28; no. 1; pp. 1 - 4 |
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| Formato: | commentary Journal Article |
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Taylor & Francis Ltd
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190352204&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190352204 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2050571X FKDR jtl: Speech, Language & Hearing issn: 2050571X maglogo: N pubinfo: dt: Dec2025 vid: 28 iid: 1 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 190352204 190352204 190352204 10.1080/2050571X.2025.2514395 190352204 ppf: 1 ppct: 3 formats: tig: atl: Making manual scoring of typed transcripts a thing of the past: a commentary on Herrmann (2025). aug: au: Bosker, Hans Rutger affil: Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands sug: subj: Speech Intelligibility Natural Language Processing Automation ab: Coding the accuracy of typed transcripts from experiments testing speech intelligibility is an arduous endeavour. A recent study in this journal [Herrmann, B. 2025. Leveraging natural language processing models to automate speech-intelligibility scoring. Speech, Language and Hearing, 28(1)] presents a novel approach for automating the scoring of such listener transcripts, leveraging Natural Language Processing (NLP) models. It involves the calculation of the semantic similarity between transcripts and target sentences using high-dimensional vectors, generated by such NLP models as ADA2, GPT2, BERT, and USE. This approach demonstrates exceptional accuracy, with negligible underestimation of intelligibility scores (by about 2-4%), numerically outperforming simpler computational tools like Autoscore and TSR. The method uniquely relies on semantic representations generated by large language models. At the same time, these models also form the Achilles heel of the technique: the transparency, accessibility, data security, ethical framework, and cost of the selected model directly impact the suitability of the NLP-based scoring method. Hence, working with such models can raise serious risks regarding the reproducibility of scientific findings. This in turn emphasises the need for fair, ethical, and evidence-based open source models. With such models, Herrmann's new tool represents a valuable addition to the speech scientist's toolbox. pubtype: Academic Journal doctype: commentary Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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