  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.
