Automatic Detection of Accent and Lexical Pronunciation Errors in Spontaneous Non-Native English Speech

Konstantinos Kyriakopoulos, Kate M. Knill, Mark J.F. Gales


Detecting individual pronunciation errors and diagnosing pronunciation error tendencies in a language learner based on their speech are important components of computer-aided language learning (CALL). The tasks of error detection and error tendency diagnosis become particularly challenging when the speech in question is spontaneous and particularly given the challenges posed by the inconsistency of human annotation of pronunciation errors. This paper presents an approach to these tasks by distinguishing between lexical errors, wherein the speaker does not know how a particular word is pronounced, and accent errors, wherein the candidate’s speech exhibits consistent patterns of phone substitution, deletion and insertion. Three annotated corpora of non-native English speech by speakers of multiple L1s are analysed, the consistency of human annotation investigated and a method presented for detecting individual accent and lexical errors and diagnosing accent error tendencies at the speaker level.


 DOI: 10.21437/Interspeech.2020-2881

Cite as: Kyriakopoulos, K., Knill, K.M., Gales, M.J. (2020) Automatic Detection of Accent and Lexical Pronunciation Errors in Spontaneous Non-Native English Speech. Proc. Interspeech 2020, 3052-3056, DOI: 10.21437/Interspeech.2020-2881.


@inproceedings{Kyriakopoulos2020,
  author={Konstantinos Kyriakopoulos and Kate M. Knill and Mark J.F. Gales},
  title={{Automatic Detection of Accent and Lexical Pronunciation Errors in Spontaneous Non-Native English Speech}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={3052--3056},
  doi={10.21437/Interspeech.2020-2881},
  url={http://dx.doi.org/10.21437/Interspeech.2020-2881}
}