The STC Keyword Search System for OpenKWS 2016 Evaluation

Yuri Khokhlov, Ivan Medennikov, Aleksei Romanenko, Valentin Mendelev, Maxim Korenevsky, Alexey Prudnikov, Natalia Tomashenko, Alexander Zatvornitsky

This paper describes the keyword search system developed by the STC team in the framework of OpenKWS 2016 evaluation. The acoustic modeling techniques included i-vectors based speaker adaptation, multilingual speaker-dependent bottleneck features, and a combination of feedforward and recurrent neural networks. To improve the language model, we augmented the training data provided by the organizers with texts generated by the character-level recurrent neural networks trained on different data sets. This led to substantial reductions in the out-of-vocabulary (OOV) and word error rates. The OOV search problem was solved with the help of a novel approach based on lattice generated phone posteriors and a highly optimized decoder. This approach outperformed familiar OOV search implementations in terms of speed and demonstrated comparable or better search quality.

The system was among the top three systems in the evaluation.

 DOI: 10.21437/Interspeech.2017-1212

Cite as: Khokhlov, Y., Medennikov, I., Romanenko, A., Mendelev, V., Korenevsky, M., Prudnikov, A., Tomashenko, N., Zatvornitsky, A. (2017) The STC Keyword Search System for OpenKWS 2016 Evaluation. Proc. Interspeech 2017, 3602-3606, DOI: 10.21437/Interspeech.2017-1212.

  author={Yuri Khokhlov and Ivan Medennikov and Aleksei Romanenko and Valentin Mendelev and Maxim Korenevsky and Alexey Prudnikov and Natalia Tomashenko and Alexander Zatvornitsky},
  title={The STC Keyword Search System for OpenKWS 2016 Evaluation},
  booktitle={Proc. Interspeech 2017},