Abstract:
A significant amount of speech data is required to develop a
robust speaker verification system, but it is difficult to find
enough development speech to match all expected conditions.
In this paper we introduce a new approach to Gaussian probabilistic
linear discriminant analysis (GPLDA) to estimate reliable
model parameters as a linearly weighted model taking
more input from the large volume of available telephone
data and smaller proportional input from limited microphone
data. In comparison to a traditional pooled training approach,
where the GPLDA model is trained over both telephone and
microphone speech, this linear-weighted GPLDA approach is
shown to provide better EER and DCF performance in microphone
and mixed conditions in both the NIST 2008 and NIST
2010 evaluation corpora. Based upon these results, we believe
that linear-weighted GPLDA will provide a better approach
than pooled GPLDA, allowing for the further improvement of
GPLDA speaker verification in conditions with limited development
data.
