Automated remote speech‐based testing of individuals with cognitive decline: Bayesian agreement of transcription accuracy

High levels of agreement were found between automated and gold-standard manual transcriptions of telephone chatbot-based semantic verbal fluency testing, particularly for word count.The qualitative features had fair levels of agreement.Word count reached high levels of discrimination between cognitively impaired and unimpaired individuals, regardless of transcription mode.Automated speech recognition for the assessment of quantitative and qualitative speech features, even when using data from telephone calls with cognitively impaired individuals in their homes, seems feasible and reliable.

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Publication Details

Journal
Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring
Published
2024-10-01
DOI
https://doi.org/10.1002/dad2.70011
Citations
7
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
2.70

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article

Automated remote speech‐based testing of individuals with cognitive decline: Bayesian agreement of transcription accuracy

Emrah Düzel, Antje Osterrath, Jens Wiltfang, Johannes Tröger et al.
7 citations
Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring
Dementia and Cognitive Impairment Research
2.70
article

Automated remote speech‐based testing of individuals with cognitive decline: Bayesian agreement of transcription accuracy

Emrah Düzel, Antje Osterrath, Jens Wiltfang, Johannes Tröger, Annika Spottke, Claudia Bartels, Sandra Roeske, Michael Wagner, Alexandra König, Elisa Mallick, Josef Priller, Björn Falkenburger, Stefanie Köhler, Okka Kimmich, Nicklas Linz, Ingo Frommann, Ingo Kilimann, Michaela Butryn, Slawek Altenstein, Daniel C. Hoffmann, Wenzel Glanz, Matthias H. Munk, Christoph Laske, Stefan Teipel, Frank Jessen
article en
7 citations

Abstract

High levels of agreement were found between automated and gold-standard manual transcriptions of telephone chatbot-based semantic verbal fluency testing, particularly for word count.The qualitative features had fair levels of agreement.Word count reached high levels of discrimination between cognitively impaired and unimpaired individuals, regardless of transcription mode.Automated speech recognition for the assessment of quantitative and qualitative speech features, even when using data from telephone calls with cognitively impaired individuals in their homes, seems feasible and reliable.

Alzheimer s & Dementia Diagnosis Assessment & Disease MonitoringVol. 16(4)
University of Bonn (DE), University of Cologne (DE), University Hospital Bonn (DE), German Center for Neurodegenerative Diseases (DE), Centre Hospitalier Universitaire de Nice (FR), UK Dementia Research Institute (GB), Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (DE), University Hospital Magdeburg (DE), Hertie Institute for Clinical Brain Research (DE), University Hospital Carl Gustav Carus (DE), University College London (GB), Technical University of Munich (DE), University of Göttingen (DE), Technische Universität Dresden (DE), Charité - Universitätsmedizin Berlin (DE), University of Tübingen (DE), Otto-von-Guericke-Universität Magdeburg (DE), University of Edinburgh (GB)
Alzheimer's Drug Discovery Foundation
Reduced inequalities, Peace, Justice and strong institutions
Openalex Percentile: Top 9%
Dementia and Cognitive Impairment Research
2.70
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