A study on separation between acoustic models and its applications

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.

interspeech05_2.pdf
PDF file

In  Proc. Interspeech

Details

TypeInproceedings
> Publications > A study on separation between acoustic models and its applications