HTK is used to create MFCC features. To compile it, run ./compile_htk.sh

Run ./link_timit.sh to create a symbolic link for TIMIT dataset under /results.

debug_mfcc has files for comparing MFCC features generated by HTK and that by python_speech_features .

Only one file is used to do this test. It's copied from TIMITcorpus/TIMIT/TRAIN/DR8/FBCG1/SX442.*, where * are PHN, TXT, WAV, WRD. .wav.sox is generated using sox SX442.WAV SX442.wav, as seen in /home/zhihaol/807/scripts/convert_wav.sh on CNBC cluster, and I renamed it to .wav.sox.

I will generate features according to Section 5.1 of Supervised Sequence Labelling with Recurrent Neural Networks, a preprint version is at http://www.cs.toronto.edu/~graves/preprint.pdf.

It's based on 3.1.5 "Step 5 - Coding the Data" of HTK official book (htkbook-3.5.alpha-1.pdf)

It's done using https://github.com/cmusphinx/sphinxtrain/blob/master/python/cmusphinx/htkmfc.py

Run PYTHONPATH=$(pwd) python feature_generation/generate_mfcc_features.py under project root to see the results.

It will generate /results/features/TIMIT_train.hdf5 and /results/features/TIMIT_test.hdf5, as well as a folder /results/features/TIMIT_train for training data of ctc_example.

It's from https://github.com/dresen/tensorflow_CTC_example.

I modified INPUT_PATH & TARGET_PATH, and minibatch size to 10 in bdlstm_train.py, and load_batched_data in utils.py.

Run PYTHONPATH=$(pwd) python ctc_example/bdlstm_train.py under project root to see the results. By 10 epochs, it should give error rate of about 0.4.

It's from http://file.ppwwyyxx.com/