Application Of Recurrent Neuron Network To Rank The List Of Hypothesis In The Systems Of Recognizing The Speech
The paper demonstrates preliminary results of the experiments on equiprobable hypothesis re-scoring with recurrent neural networks (RNN). RNNs proved to be successful for language modelling on various tasks for English including speech recognition and phrase completion but their applicability to inflictive languages is not well studied yet. However, for now we trained the model only on lemmas with additional morphological information to decrease data sparseness. We demonstrate that the model performs better than the popular 5-gram model with Knesser-Ney smoothing.