The NTNU System at the Interspeech 2020 Non-Native Children’s Speech ASR Challenge

Tien-Hong Lo, Fu-An Chao, Shi-Yan Weng, Berlin Chen


This paper describes the NTNU ASR system participating in the Interspeech 2020 Non-Native Children’s Speech ASR Challenge supported by the SIG-CHILD group of ISCA. This ASR shared task is made much more challenging due to the coexisting diversity of non-native and children speaking characteristics. In the setting of closed-track evaluation, all participants were restricted to develop their systems merely based on the speech and text corpora provided by the organizer. To work around this under-resourced issue, we built our ASR system on top of CNN-TDNNF-based acoustic models, meanwhile harnessing the synergistic power of various data augmentation strategies, including both utterance- and word-level speed perturbation and spectrogram augmentation, alongside a simple yet effective data-cleansing approach. All variants of our ASR system employed an RNN-based language model to rescore the first-pass recognition hypotheses, which was trained solely on the text dataset released by the organizer. Our system with the best configuration came out in second place, resulting in a word error rate (WER) of 17.59%, while those of the top-performing, second runner-up and official baseline systems are 15.67%, 18.71%, 35.09%, respectively.


 DOI: 10.21437/Interspeech.2020-1990

Cite as: Lo, T., Chao, F., Weng, S., Chen, B. (2020) The NTNU System at the Interspeech 2020 Non-Native Children’s Speech ASR Challenge. Proc. Interspeech 2020, 250-254, DOI: 10.21437/Interspeech.2020-1990.


@inproceedings{Lo2020,
  author={Tien-Hong Lo and Fu-An Chao and Shi-Yan Weng and Berlin Chen},
  title={{The NTNU System at the Interspeech 2020 Non-Native Children’s Speech ASR Challenge}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={250--254},
  doi={10.21437/Interspeech.2020-1990},
  url={http://dx.doi.org/10.21437/Interspeech.2020-1990}
}