Spoken English Intelligibility Remediation with Pocketsphinx Alignment and Feature Extraction Improves Substantially Over the State of the Art

@article{Gao2018SpokenEI,
  title={Spoken English Intelligibility Remediation with Pocketsphinx Alignment and Feature Extraction Improves Substantially Over the State of the Art},
  author={Yuan Gao and Brij Mohan Lal Srivastava and James Salsman},
  journal={2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)},
  year={2018},
  pages={924-927}
}
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… 

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