Abstract
In this paper we present the latest improvements to the Russian spontaneous speech recognition system developed in Speech Technology Center (STC). Significant word error rate (WER) reduction was obtained by applying hypothesis rescoring with sophisticated language models. These were the Recurrent Neural Network Language Model and regularized Long-Short Term Memory Language Model. For acoustic modeling we used the deep neural network (DNN) trained with speaker-dependent bottleneck features, similar to our previous system. This DNN was combined with the deep Bidirectional Long Short-Term Memory acoustic model by the use of score fusion. The resulting system achieves WER of 16.4 %, with an absolute reduction of 8.7 % and relative reduction of 34.7 % compared to our previous system result on this test set.
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Acknowledgement
This work was financially supported by the Ministry of Education and Science of the Russian Federation, Contract 14.579.21.0057 (ID RFMEFI57914X0057).
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Medennikov, I., Prudnikov, A. (2016). Advances in STC Russian Spontaneous Speech Recognition System. In: Ronzhin, A., Potapova, R., Németh, G. (eds) Speech and Computer. SPECOM 2016. Lecture Notes in Computer Science(), vol 9811. Springer, Cham. https://doi.org/10.1007/978-3-319-43958-7_13
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DOI: https://doi.org/10.1007/978-3-319-43958-7_13
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