  Language models (LM) play an important role in large vocabulary continuous
speech recognition (LVCSR). However, traditional language models only predict
next single word with given history, while the consecutive predictions on a
sequence of words are usually demanded and useful in LVCSR. The mismatch
between the single word prediction modeling in trained and the long term
sequence prediction in read demands may lead to the performance degradation. In
this paper, a novel enhanced long short-term memory (LSTM) LM using the future
vector is proposed. In addition to the given history, the rest of the sequence
will be also embedded by future vectors. This future vector can be incorporated
with the LSTM LM, so it has the ability to model much longer term sequence
level information. Experiments show that, the proposed new LSTM LM gets a
better result on BLEU scores for long term sequence prediction. For the speech
recognition rescoring, although the proposed LSTM LM obtains very slight gains,
the new model seems obtain the great complementary with the conventional LSTM
LM. Rescoring using both the new and conventional LSTM LMs can achieve a very
large improvement on the word error rate.
