Abstract:
In practical applications, speaker verification systems have to be developed and trained using data which is outside the
domain of the intended application as the collection of significant amount of in-domain data could be difficult. Experimental
studies have found that when a GPLDA system is trained using out-domain data, it significantly affects the speaker
verification performance due to the mismatch between development data and evaluation data. This paper proposes several
unsupervised inter-dataset variability compensation approaches for the purpose of improving the performance of GPLDA
systems trained using out-domain data. We show that when GPLDA is trained using out-domain data, we can improve the
performance by as much as 39% by using by score normalisation using small amounts of in-domain data. Also in situations
where rich out-domain data and only limited in-domain data are available, a pooled-linear-weighted technique to estimate
the GPLDA parameters shows 35% relative improvements in equal error rate (EER) on int–int conditions. We also propose
a novel inter-dataset covariance normalization (IDCN) approach to overcome in- and out-domain data mismatch problem.
Our unsupervised IDCN-compensated GPLDA system shows 14 and 25% improvement respectively in EER over out-domain
GPLDA speaker verification on tel–tel and int–int training–testing conditions. We provide intuitive explanations as to why
these inter-dataset variability compensation approaches provide improvements to speaker verification accuracy.
