International Conference on Speech and Computer

SPECOM 2015: Speech and Computer pp 42-50 | Cite as

A Comparison of RNN LM and FLM for Russian Speech Recognition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9319)


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.


Recurrent neural networks Language models Automatic speech recognition Russian speech 



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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.SPIIRASSt. PetersburgRussia
  2. 2.SUAISt. PetersburgRussia
  3. 3.University ITMOSt. PetersburgRussia

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