ISCA Archive Interspeech 2014
ISCA Archive Interspeech 2014

Using deep neural networks to improve proficiency assessment for children English language learners

Angeliki Metallinou, Jian Cheng

We investigated the use of context-dependent deep neural network hidden Markov models, or CD-DNN-HMMs, to improve speech recognition performance for a better assessment of children English language learners (ELLs). The ELL data used in the present study was obtained from a large language assessment project administered in schools in a U.S. state. Our DNN-based speech recognition system, built using rectified linear units (ReLU), greatly outperformed recognition accuracy of Gaussian mixture models (GMM)-HMMs, even when the latter models were trained with eight times more data. Large improvement was observed for cases of noisy and/or unclear responses, which are common in ELL children speech. We further explored the use of content and manner-of-speaking features, derived from the speech recognizer output, for estimating spoken English proficiency levels. Experimental results show that the DNN-based recognition approach achieved 31% relative WER reduction when compared to GMM-HMMs. This further improved the quality of the extracted features and final spoken English proficiency scores, and increased overall automatic assessment performance to the human performance level, for various open-ended spoken language tasks.


doi: 10.21437/Interspeech.2014-358

Cite as: Metallinou, A., Cheng, J. (2014) Using deep neural networks to improve proficiency assessment for children English language learners. Proc. Interspeech 2014, 1468-1472, doi: 10.21437/Interspeech.2014-358

@inproceedings{metallinou14_interspeech,
  author={Angeliki Metallinou and Jian Cheng},
  title={{Using deep neural networks to improve proficiency assessment for children English language learners}},
  year=2014,
  booktitle={Proc. Interspeech 2014},
  pages={1468--1472},
  doi={10.21437/Interspeech.2014-358},
  issn={2308-457X}
}