# config for high-resolution MFCC features, 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=40     # there is no dimensionality reduction.
  --low-freq=40    # low cutoff frequency for mel bins
  --high-freq=-200 # high cutoff frequently, relative to Nyquist of 4000 (=3800)