We study separation between models of speech attributes. A
good measure of separation usually serves as a key indicator
of the discrimination power of these speech models because it
can often be used to indirectly determine the performance of
speech recognition and verification systems. In this study, we
use a probabilistic distance, called generalized log likelihood
ratio (GLLR), to measure the separation between a model of a
target speech attribute and models of its competing attributes.
We illustrate five applications to compare separations among
models obtained over multiple levels of discrimination
capabilities, at various degrees of acoustic definitions and
resolutions, under mismatched training and testing conditions,
and with different training criteria and speech parameters. We
demonstrate that the well-known GLLR distance and its
corresponding histograms also provide a good utility to
qualitatively and quantitatively characterize the properties of
trained models without performing large scale speech
recognition and verification experiments.