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This paper presents our contribution to the 3rd CHiME Speech Separation andRecognition Challenge. Our system uses Bidirectional Long Short-Term Memory(BLSTM) Recurrent Neural Networks (RNNs) for Single-channel Speech Enhancement(SSE). Networks are trained to predict clean speech as well as noise featuresfrom noisy speech features. In addition, the system applies two methods ofdereverberation on the 6-channel recordings of the challenge. The first is thePhase-Error based Filtering (PEF) that uses time-varying phase-error filtersbased on estimated time-difference of arrival of the speech source and thephases of the microphone signals. The second is the Correlation Shaping (CS)that applies a reduction of the long-term correlation energy in reverberantspeech. The Linear Prediction (LP) residual is processed to suppress thelong-term correlation. Furthermore, the system employs a LSTM Language Model(LM) to perform N-best rescoring of recognition hypotheses. Using the proposedmethods, an improved Word Error Rate (WER) of 24.38% is achieved over the realeval test set. This is around 25% relative improvement over the challengebaseline.

The ICSTM+TUM+UP Approach to the 3rd CHIME Challenge: Single-Channel  LSTM Speech Enhancement with Multi-Channel Correlation Shaping  Dereverberation and LSTM Language Models