Language Models with RNNs for Rescoring Hypotheses of Russian ASR
In this paper, we describe a research of recurrent neural networks (RNNs) for language modeling in large vocabulary continuous speech recognition for Russian. We experimented with recurrent neural networks with different number of units in the hidden layer. RNN based and 3 gram language models (LMs) were trained using the text corpus of 350M words. Obtained RNN based language models were used for N best list rescoring for automatic continuous Russian speech recognition. We tested also a linear interpolation of RNN LMs with the baseline 3 gram LM and achieved 14 relative reduction of the word error rate (WER) with respect to the baseline 3 gram model.
ISNN