Automatic Assessment of Non-Native Prosody by Measuring Distances on Prosodic Label Sequences

David Escudero-Mancebo, César González-Ferreras, Lourdes Aguilar, Eva Estebas-Vilaplana


The aim of this paper is to investigate how automatic prosodic labeling systems contribute to the evaluation of non-native pronunciation. In particular, it examines the efficiency of a group of metrics to evaluate the prosodic competence of non-native speakers, based on the information provided by sequences of labels in the analysis of both native and non-native speech. A group of Sp_ToBI labels were obtained by means of an automatic labeling system for the speech of native and non-native speakers who read the same texts. The metrics assessed the differences in the prosodic labels for both speech samples. The results showed the efficiency of the metrics to set apart both groups of speakers. Furthermore, they exhibited how non-native speakers (American and Japanese speakers) improved their Spanish productions after doing a set of listening and repeating activities. Finally, this study also shows that the results provided by the metrics are correlated with the scores given by human evaluators on the productions of the different speakers.


 DOI: 10.21437/Interspeech.2017-366

Cite as: Escudero-Mancebo, D., González-Ferreras, C., Aguilar, L., Estebas-Vilaplana, E. (2017) Automatic Assessment of Non-Native Prosody by Measuring Distances on Prosodic Label Sequences. Proc. Interspeech 2017, 1442-1446, DOI: 10.21437/Interspeech.2017-366.


@inproceedings{Escudero-Mancebo2017,
  author={David Escudero-Mancebo and César González-Ferreras and Lourdes Aguilar and Eva Estebas-Vilaplana},
  title={Automatic Assessment of Non-Native Prosody by Measuring Distances on Prosodic Label Sequences},
  year=2017,
  booktitle={Proc. Interspeech 2017},
  pages={1442--1446},
  doi={10.21437/Interspeech.2017-366},
  url={http://dx.doi.org/10.21437/Interspeech.2017-366}
}