dc.contributor.author |
Rahman, M.H. |
|
dc.contributor.author |
Ahilan, K. |
|
dc.contributor.author |
Dean, D. |
|
dc.contributor.author |
Sridharan, S. |
|
dc.date.accessioned |
2021-03-15T07:38:36Z |
|
dc.date.accessioned |
2022-06-27T10:02:21Z |
|
dc.date.available |
2021-03-15T07:38:36Z |
|
dc.date.available |
2022-06-27T10:02:21Z |
|
dc.date.issued |
2015 |
|
dc.identifier.citation |
Rahman, M. H., Kanagasundaram, A., Dean, D., & Sridharan, S. (2015). Dataset-invariant covariance normalization for out-domain PLDA speaker verification. In Proceedings of the 16th Annual Conference of the International Speech Communication Association, Interspeech 2015 (pp. 1017-1021). International Speech Communication Association. |
en_US |
dc.identifier.uri |
http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1876 |
|
dc.description.abstract |
In this paper we introduce a novel domain-invariant covariance
normalization (DICN) technique to relocate both in-domain and
out-domain i-vectors into a third dataset-invariant space, providing
an improvement for out-domain PLDA speaker verification
with a very small number of unlabelled in-domain adaptation
i-vectors. By capturing the dataset variance from a global
mean using both development out-domain i-vectors and limited
unlabelled in-domain i-vectors, we could obtain domaininvariant
representations of PLDA training data. The DICNcompensated
out-domain PLDA system is shown to perform as
well as in-domain PLDA training with as few as 500 unlabelled
in-domain i-vectors for NIST-2010 SRE and 2000 unlabelled
in-domain i-vectors for NIST-2008 SRE, and considerable relative
improvement over both out-domain and in-domain PLDA
development if more are available. |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
speaker verification |
en_US |
dc.subject |
PLDA |
en_US |
dc.title |
Dataset-Invariant Covariance Normalization for Out-domain PLDA Speaker Verification |
en_US |
dc.type |
Article |
en_US |