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 B. M. L. 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… Expand

References

SHOWING 1-10 OF 16 REFERENCES
Pronunciation analysis for children with speech sound disorders
  • Shiran Dudy, Meysam Asgari, A. Kain
  • Computer Science, Medicine
  • 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2015
Computer-assisted pronunciation training: From pronunciation scoring towards spoken language learning
  • Nancy F. Chen, Haizhou Li
  • Computer Science
  • 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)
  • 2016
Automatic analysis of pronunciations for children with speech sound disorders
A Two-Pass Framework of Mispronunciation Detection and Diagnosis for Computer-Aided Pronunciation Training
Phone-level pronunciation scoring and assessment for interactive language learning
Pocketsphinx: A Free, Real-Time Continuous Speech Recognition System for Hand-Held Devices
Speech recognition system for Handheld devices
...
1
2
...