Title: Spoken English Intelligibility Remediation with PocketSphinx Alignment and Feature Extraction Improves Substantially over the State of the Art,
Abstract: We 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 percent
agreement with the accuracy of Amazon Mechanical Turk crowdworker
transcriptions, up from 75 percent 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.