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

Noise-Robust ASR for the third 'CHiME' Challenge Exploiting  Time-Frequency Masking based Multi-Channel Speech Enhancement and Recurrent  Neural Network