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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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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