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53 строки
1.9 KiB
Plaintext
53 строки
1.9 KiB
Plaintext
This is a kaldi setup for 3rd CHiME challenge.
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See http://spandh.dcs.shef.ac.uk/chime_challenge/ for more detailed information.
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If you use these data in a publication, please cite:
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Jon Barker, Ricard Marxer, Emmanuel Vincent, and Shinji Watanabe, The
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third 'CHiME' Speech Separation and Recognition Challenge: Dataset,
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task and baselines, submitted to IEEE 2015 Automatic Speech Recognition
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and Understanding Workshop (ASRU), 2015.
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Quick instruction:
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1) Download CHiME3 data
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Check the download page of http://spandh.dcs.shef.ac.uk/chime_challenge/
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2) move to Kaldi CHiME3 directory, e.g.,
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cd kaldi-trunk/egs/chime3/s5
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3) specify CHiME3 root directory in run.sh e.g.,
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chime3_data=<your CHiME3 directory>/CHiME3
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4) execute run.sh
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./run.sh
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4*) we suggest to use the following command to save the main log file
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nohup ./run.sh > run.log
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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
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local/run_gmm.sh <enhancement method> <enhanced speech directory>
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local/run_dnn.sh <enhancement method> <enhanced speech directory>
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local/run_lmrescore.sh <your CHiME3 directory> <enhancement method>
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You can put <enhanced speech directory> in your working directory.
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But please make sure to use the same directory structure and naming convention with those of the
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example enhanced speech directory in CHiME3/data/audio/16kHz/enhanced
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You don't have to execute local/run_init.sh twice.
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6) You can find result at
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enhan=<enhancement method>
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GMM clean training: exp/tri3b_tr05_orig_clean/best_wer_$enhan.result
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GMM multi training: exp/tri3b_tr05_multi_$enhan/best_wer_$enhan.result
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DNN multi training: exp/tri4a_dnn_tr05_multi_${enhan}_smbr_i1lats/best_wer_${enhan}.result
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DNN multi training with LM rescoring: exp/tri4a_dnn_tr05_multi_${enhan}_smbr_i1lats_lmrescore/best_wer_${enhan}_rnnlm_5k_h300_w0.5_n100.result
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Note that training on clean data means original WSJ0 data only (no booth data)
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