Learning Neural Network Representations Using Cross-Lingual Bottleneck Features with Word-Pair Information

Yougen Yuan, Cheung-Chi Leung, Lei Xie, Bin Ma, Haizhou Li

We assume that only word pairs identified by human are available in a low-resource target language. The word pairs are parameterized by a bottleneck feature (BNF) extractor that is trained using transcribed data in a high-resource language. The cross-lingual BNFs of the word pairs are used for training another neural network to generate a new feature representation in the target language. Pairwise learning of frame-level and word-level feature representations are investigated. Our proposed feature representations were evaluated in a word discrimination task on the Switchboard telephone speech corpus. Our learned features could bring 27.5% relative improvement over the previously best reported result on the task.

doi: 10.21437/Interspeech.2016-317

Cite as: Yuan, Y., Leung, C.-C., Xie, L., Ma, B., Li, H. (2016) Learning Neural Network Representations Using Cross-Lingual Bottleneck Features with Word-Pair Information. Proc. Interspeech 2016, 788-792, doi: 10.21437/Interspeech.2016-317

@inproceedings{yuan16_interspeech,
  author={Yougen Yuan and Cheung-Chi Leung and Lei Xie and Bin Ma and Haizhou Li},
  title={{Learning Neural Network Representations Using Cross-Lingual Bottleneck Features with Word-Pair Information}},
  year=2016,
  booktitle={Proc. Interspeech 2016},
  pages={788--792},
  doi={10.21437/Interspeech.2016-317}
}