Speech enhancement using beamforming and non negative matrix factorization for robust speech recognition in the CHiME-3 challenge

In this paper we present our contribution to the third CHiME challenge on speech separation and recognition for noisy multi-channel recordings. The use-case of the challenge consists in single speaker utterances recorded in highly non-stationary noisy environments using a 6-microphone array mounted on a tablet computer. The front-end of our system is performing speech enhancement by cascading a cross-correlation-based channel selection, Signal Dependent MVDR beamforming and online source separation based on sparse NMF. The back-end module is a state-of-the-art speech recognition system with DNN acoustic models trained on fMLLR features and a RNN Language Model. Our system reaches an overall WER of 11.94% on real test recordings, achieving a relative improvement of 65% compared to the baseline system.