Transcription of Recurrent Neural Network Based Language Model
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Recurrent Neural Network Based Language Model
Recurrent Neural Network Based Language modelTom a s Mikolov1,2, Martin Karafi at1, Luk a s Burget1, Jan Honza Cernock y1, Sanjeev Khudanpur21 Speech@FIT, Brno University of Technology, Czech Republic2 Department of Electrical and Computer Engineering, Johns Hopkins University, new Recurrent Neural Network Based Language Model (RNNLM) with applications to speech recognition is presented. Re-sults indicate that it is possible to obtain around 50% reductionof perplexity by using mixture of several RNN LMs, comparedto a state of the art backoff Language Model . Speech recognitionexperiments show around 18% reduction of word error rate onthe Wall Street Journal task when comparing models trained onthe same amount of data, and around 5% on the much harderNIST RT05 task, even when the backoff Model is trained onmuch more data than the RNN LM.
Recurrent neural network based language model ... It is questionable if there has been any significant progress in language modeling over simple n-gram models (see for ex-ample [2] for review of advanced techniques). ... we have used an architecture that is usually called a simple recurrent neural network or Elman network [7].
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