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A Comparison of RNN LM and FLM for Russian Speech Recognition

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Speech and Computer (SPECOM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9319))

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Abstract

In the paper, we describe a research of recurrent neural network (RNN) language model (LM) for N-best list rescoring for automatic continuous Russian speech recognition and make a comparison of it with factored language model (FLM). We tried RNN with different number of units in the hidden layer. For FLM creation, we used five linguistic factors: word, lemma, stem, part-of-speech, and morphological tag. All models were trained on the text corpus of 350M words. Also we made linear interpolation of RNN LM and FLM with the baseline 3-gram LM. We achieved the relative WER reduction of 8 % using FLM and 14 % relative WER reduction using RNN LM with respect to the baseline model.

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Acknowledgments

This research is partially supported by the Council for Grants of the President of Russia (Projects No. MK-5209.2015.8 and MD-3035.2015.8), by the Russian Foundation for Basic Research (Projects No. 15-07-04415 and 15-07-04322), and by the Government of the Russian Federation (Grant No. 074-U01).

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Authors and Affiliations

  1. SPIIRAS, 39, 14th Line, St. Petersburg, 199178, Russia

    Irina Kipyatkova & Alexey Karpov

  2. SUAI, 67, Bolshaya Morskaia, St. Petersburg, 199000, Russia

    Irina Kipyatkova

  3. University ITMO, 49 Kronverksky Pr., St. Petersburg, 197101, Russia

    Alexey Karpov

Authors
  1. Irina Kipyatkova
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  2. Alexey Karpov
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Corresponding author

Correspondence to Irina Kipyatkova .

Editor information

Editors and Affiliations

  1. SPIIRAS, Saint-Petersburg, Russia

    Andrey Ronzhin

  2. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

  3. University of Patras, Patras, Greece

    Nikos Fakotakis

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Kipyatkova, I., Karpov, A. (2015). A Comparison of RNN LM and FLM for Russian Speech Recognition. In: Ronzhin, A., Potapova, R., Fakotakis, N. (eds) Speech and Computer. SPECOM 2015. Lecture Notes in Computer Science(), vol 9319. Springer, Cham. https://doi.org/10.1007/978-3-319-23132-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-23132-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23131-0

  • Online ISBN: 978-3-319-23132-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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