Language modeling with highway LSTM

Language models (LMs) based on Long Short Term Memory (LSTM) have shown good gains in many automatic speech recognition tasks. In this paper, we extend an LSTM by adding highway networks inside an LSTM and use the resulting Highway LSTM (HW-LSTM) model for language modeling. The added highway networks increase the depth in the time dimension. Since a typical LSTM has two internal states, a memory cell and a hidden state, we compare various types of HW-LSTM by adding highway networks onto the memory cell and/or the hidden state. Experimental results on English broadcast news and conversational telephone speech recognition show that the proposed HW-LSTM LM improves speech recognition accuracy on top of a strong LSTM LM baseline. We report 5.1% and 9.9% on the Switchboard and CallHome subsets of the Hub5 2000 evaluation, which reaches the best performance numbers reported on these tasks to date.

1. Introduction

Deep learning based approaches, especially recurrent neural networks and their variants, have been one of the hottest topics in language modeling research for the past few years. Long Short Term Memory (LSTM) based recurrent language models (LMs) have shown significant perplexity gains on well established benchmarks such as the Penn Tree Bank [1] and the more recent One Billion corpus [2]. These results validate the potential of deep learning and recurrent models as being key to further progress in the field of language modeling. Since LMs are one of the core components of natural language processing (NLP) tasks such as automatic speech recognition (ASR) and Machine Translation (MT), improved language modeling techniques have sometimes translated to improvements in overall system performance for these tasks [3]–[5].