Factor Analysis Modelling for Speaker Verification with Short Utterances

Abstract

This paper examines combining both relevance MAP and subspace
speaker adaptation processes to train GMM speaker models
for use in speaker verification systems with a particular focus
on short utterance lengths. The subspace speaker adaptation
method involves developing a speaker GMM mean supervector
as the sum of a speaker-independent prior distribution and
a speaker dependent offset constrained to lie within a low-rank
subspace, and has been shown to provide improvements in accuracy
over ordinary relevance MAP when the amount of training
data is limited.
It is shown through testing on NIST SRE data that combining
the two processes provides speaker models which lead
to modest improvements in verification accuracy for limited
data situations, in addition to improving the performance of the
speaker verification system when a larger amount of available
training data is available.