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We assess speech intelligibility from listener transcripts by measuring phoneme errors and word boundary identification errors using automated algorithms. The estimated metrics have been shown as reliable as human codings. They are also able to explain perceptual variations of speech intelligibility.

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This page is a summary of: Objective Intelligibility Assessment by Automated Segmental and Suprasegmental Listening Error Analysis, Journal of Speech Language and Hearing Research, September 2019, American Speech-Language-Hearing Association (ASHA),
DOI: 10.1044/2019_jslhr-s-19-0119.
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