# config for high-resolution MFCC features extracted at double the normal frame
# rate, intended for neural network training.  Note: we keep all cepstra, so it
# has the same info as filterbank features, but MFCC is more easily compressible
# (because less correlated) which is why we prefer this method.
--use-energy=false   # use average of log energy, not energy.
--sample-frequency=8000 #  Switchboard is sampled at 8kHz
--num-mel-bins=40     # similar to Google's setup.
--num-ceps=10     # for the higher-frequency-resolution mfcc coefficients, we'll use
                  # a larger window size of 25ms and the normal window.
--low-freq=40    # low cutoff frequency for mel bins
--high-freq=-200 # high cutoff frequently, relative to Nyquist of 4000 (=3800)
--frame-length=17 # shorter than normal (25ms) frame length.... the shortest we can
                  # go without the FFT becoming lower resolution which might cause
                  # problems
--window-type=hanning # additionally making the context shorter by using a more aggressively tapering window.
--frame-shift=5  # half the normal frame shift
