Global impostor selection for DBNs in multi-session i-vector speaker recognition
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10.1007/978-3-319-13623-3_10
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/27397
Tipus de documentArticle
Data publicació2014-11-19
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Abstract
An effective global impostor selection method is proposed in
this paper for discriminative Deep Belief Networks (DBN) in the context
of a multi-session i-vector based speaker recognition. The proposed
method is an iterative process in which in each iteration the whole
impostor i-vector dataset is divided randomly into two subsets. The
impostors in one subset which are closer to each impostor in another
subset are selected and impostor frequencies are computed. At the
end, those impostors with higher frequencies will be the global selected
ones. They are then clustered and the centroids are considered as the
final impostors for the DBN speaker models. The advantage of the
proposed method is that in contrary to other similar approaches, only
the background i-vector dataset is employed. The experimental results
are performed on the NIST 2014 i-vector challenge dataset and it is
shown that the proposed selection method improves the performance
of the DBN-based system in terms of minDCF by 7% and the whole
system outperforms the baseline in the challenge by more than 22%
relative improvement.
CitacióGhahabi, O.; Hernando, J. Global impostor selection for DBNs in multi-session i-vector speaker recognition. "Lecture notes in computer science", 19 Novembre 2014, vol. LNAI 8854, p. 89-98.
ISSN0302-9743
Versió de l'editorhttp://link.springer.com/chapter/10.1007%2F978-3-319-13623-3_10
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