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CHiME3 Challenge task
This is a kaldi setup for 3rd CHiME challenge. See http://spandh.dcs.shef.ac.uk/chime_challenge/ for more detailed information. If you use these data in a publication, please cite: Jon Barker, Ricard Marxer, Emmanuel Vincent, and Shinji Watanabe, The third 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines, submitted to IEEE 2015 Automatic Speech Recognition and Understanding Workshop (ASRU), 2015. Quick instruction: 1) Download CHiME3 data Check the download page of http://spandh.dcs.shef.ac.uk/chime_challenge/ 2) move to Kaldi CHiME3 directory, e.g., cd kaldi-trunk/egs/chime3/s5 3) specify CHiME3 root directory in run.sh e.g., chime3_data=<your CHiME3 directory>/CHiME3 4) execute run.sh ./run.sh 4*) we suggest to use the following command to save the main log file nohup ./run.sh > run.log 5) if you have your own enhanced speech data for training and test data, you can evaluate the performance of GMM and DNN systems by local/run_gmm.sh <enhancement method> <enhanced speech directory> local/run_dnn.sh <enhancement method> <enhanced speech directory> You don't have to execute local/run_init.sh twice. 6) You can find result at enhan=<enhancement method> GMM clean training: exp/tri3b_tr05_orig_clean/best_wer_$enhan.result GMM multi training: exp/tri3b_tr05_multi_$enhan/best_wer_$enhan.result DNN multi training: exp/tri4a_dnn_tr05_multi_${enhan}_smbr_i1lats/best_wer_${enhan}.result Note that training on clean data means original WSJ0 data only (no booth data)
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