  In this paper, we describe the Maximum Uniformity of Distribution (MUD)
algorithm with the power-law nonlinearity. In this approach, we hypothesize
that neural network training will become more stable if feature distribution is
not too much skewed. We propose two different types of MUD approaches: power
function-based MUD and histogram-based MUD. In these approaches, we first
obtain the mel filterbank coefficients and apply nonlinearity functions for
each filterbank channel. With the power function-based MUD, we apply a
power-function based nonlinearity where power function coefficients are chosen
to maximize the likelihood assuming that nonlinearity outputs follow the
uniform distribution. With the histogram-based MUD, the empirical Cumulative
Density Function (CDF) from the training database is employed to transform the
original distribution into a uniform distribution. In MUD processing, we do not
use any prior knowledge (e.g. logarithmic relation) about the energy of the
incoming signal and the perceived intensity by a human. Experimental results
using an end-to-end speech recognition system demonstrate that power-function
based MUD shows better result than the conventional Mel Filterbank Cepstral
Coefficients (MFCCs). On the LibriSpeech database, we could achieve 4.02 % WER
on test-clean and 13.34 % WER on test-other without using any Language Models
(LMs). The major contribution of this work is that we developed a new algorithm
for designing the compressive nonlinearity in a data-driven way, which is much
more flexible than the previous approaches and may be extended to other domains
as well.
