  Triangular, overlapping Mel-scaled filters ("f-banks") are the current
standard input for acoustic models that exploit their input's time-frequency
geometry, because they provide a psycho-acoustically motivated time-frequency
geometry for a speech signal. F-bank coefficients are provably robust to small
deformations in the scale. In this paper, we explore two ways in which filter
banks can be adjusted for the purposes of speech recognition. First, triangular
filters can be replaced with Gabor filters, a compactly supported filter that
better localizes events in time, or Gammatone filters, a
psychoacoustically-motivated filter. Second, by rearranging the order of
operations in computing filter bank features, features can be integrated over
smaller time scales while simultaneously providing better frequency resolution.
We make all feature implementations available online through open-source
repositories. Initial experimentation with a modern end-to-end CNN phone
recognizer yielded no significant improvements to phone error rate due to
either modification. The result, and its ramifications with respect to learned
filter banks, is discussed.
