Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   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.
['T. T. Vu', 'Benjamin Bigot', 'Eng Siong Chng']
I am writing a research paper on a idea Speech enhancement using beamforming and non negative matrix factorization for robust speech recognition in the CHiME-3 challenge
[{"from": "human", "value": "I am writing a research paper on a idea Speech enhancement using beamforming and non negative matrix factorization for robust speech recognition in the CHiME-3 challenge"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   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."}]