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- W2912595557 abstract "Speech perception studies typically rely on trained research assistants to score orthographic listener transcripts for words correctly identified. While the accuracy of the human scoring protocol has been validated with strong intra- and inter-rater reliability, the process of hand-scoring the transcripts is time-consuming and resource intensive. Here, an open-source computer-based tool for automated scoring of listener transcripts is built (Autoscore) and validated on three different human-scored data sets. Results show that not only is Autoscore highly accurate, achieving approximately 99% accuracy, but extremely efficient. Thus, Autoscore affords a practical research tool, with clinical application, for scoring listener intelligibility of speech." @default.
- W2912595557 created "2019-02-21" @default.
- W2912595557 creator A5034588389 @default.
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- W2912595557 date "2019-01-01" @default.
- W2912595557 modified "2023-10-06" @default.
- W2912595557 title "Autoscore: An open-source automated tool for scoring listener perception of speech" @default.
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- W2912595557 doi "https://doi.org/10.1121/1.5087276" @default.
- W2912595557 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6347573" @default.
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