W279　use　automatic　speech　recognition　to　assess　spoken　English　learner　pronunciation　based　on　the　authentic　intelligibility　of　the　learners＇　spoken　responses　determined　from　support　vector　machine　(SVM)　classifier　or　deep　learning　neural　network　model　predictions　of　transcription　correctness.　Using　numeric　features　produced　by　PocketSphinx　alignment　mode　and　many　recognition　passes　searching　for　the　substitution　and　deletion　of　each　expected　phoneme　and　insertion　of　unexpected　phonemes　in　sequence,　the　SVM　models　achieve　82%　agreement　with　the　accuracy　of　Amazon　Mechanical　Turk　crowdworker　transcriptions,　up　from　75%　reported　by　multiple　independent　researchers.　Using　such　features　with　SVM　classifier　probability　prediction　models　can　help　computer-aided　pronunciation　teaching　(CAPT)　systems　provide　intelligibility　remediation.　©　2018　IEEE.