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Deep Neural Networks in Russian Speech Recognition

  • Nikita Markovnikov
  • Irina Kipyatkova
  • Alexey Karpov
  • Andrey Filchenkov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 789)

Abstract

Hybrid speech recognition systems incorporating deep neural networks (DNNs) with Hidden Markov Models/Gaussian Mixture Models have achieved good results. We propose applying various DNNs in automatic recognition of Russian continuous speech. We used different neural network models such as Convolutional Neural Networks (CNNs), modifications of Long short-term memory (LSTM), Residual Networks and Recurrent Convolutional Networks (RCNNs). The presented model achieved \(7.5\%\) reducing of word error rate (WER) compared with Kaldi baseline. Experiments are performed with extra-large vocabulary (more than 30 h) of Russian speech.

Keywords

Deep learning Russian speech Speech recognition Acoustic models 

Notes

Acknowledgments

This research is partially supported by the Council for Grants of the President of the Russian Federation (project No. MK-1000.2017.8) and by the Russian Foundation for Basic Research (project No. 15-07-04322).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Nikita Markovnikov
    • 1
    • 2
  • Irina Kipyatkova
    • 2
  • Alexey Karpov
    • 2
  • Andrey Filchenkov
    • 1
  1. 1.ITMO UniversitySaint-PetersburgRussia
  2. 2.SPIIRAS InstituteSaint-PetersburgRussia

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