  In this paper, the Lingban entry to the third 'CHiME' speech separation and
recognition challenge is presented. A time-frequency masking based speech
enhancement front-end is proposed to suppress the environmental noise utilizing
multi-channel coherence and spatial cues. The state-of-the-art speech
recognition techniques, namely recurrent neural network based acoustic and
language modeling, state space minimum Bayes risk based discriminative acoustic
modeling, and i-vector based acoustic condition modeling, are carefully
integrated into the speech recognition back-end. To further improve the system
performance by fully exploiting the advantages of different technologies, the
final recognition results are obtained by lattice combination and rescoring.
Evaluations carried out on the official dataset prove the effectiveness of the
proposed systems. Comparing with the best baseline result, the proposed system
obtains consistent improvements with over 57% relative word error rate
reduction on the real-data test set.
