Articulation Rate as a Metric in Spoken Language Assessment

Calbert Graham, Francis Nolan


Automated evaluation of non-native pronunciation provides a consistent and more cost-efficient alternative to human evaluation. To that end, there is considerable interest in deriving metrics that are based on the cues human listeners use to judge pronunciation. Previous research reported the use of phonetic features such as vowel characteristics in automated spoken language evaluation. The present study extends this line of work on the significance of phonetic features in automated evaluation of L2 speech (both assessment and feedback). Predictive modelling techniques examined the relationship between various articulation rate metrics one the one hand, and the proficiency and L1 background of non-native English speakers on the other. It was found that the optimal predictive model was one in which the phonetic details of phoneme articulation were factored in the analysis of articulation rate. Model performance varied also according to the L1 background of speakers. The implications for assessment and feedback are discussed.


 DOI: 10.21437/Interspeech.2019-2098

Cite as: Graham, C., Nolan, F. (2019) Articulation Rate as a Metric in Spoken Language Assessment. Proc. Interspeech 2019, 3564-3568, DOI: 10.21437/Interspeech.2019-2098.


@inproceedings{Graham2019,
  author={Calbert Graham and Francis Nolan},
  title={{Articulation Rate as a Metric in Spoken Language Assessment}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={3564--3568},
  doi={10.21437/Interspeech.2019-2098},
  url={http://dx.doi.org/10.21437/Interspeech.2019-2098}
}