Sequential model adaptation for speaker verification
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
International Speech Communication AssociationDate
2013Citation
INTERSPEECH 2013: 14th Annual Conference of the International Speech Communication Association. Ed. F. Bimbot, C. Cerisara, C. Fougeron, G. Gravier, L. Lamel, F. Pellegrino, and P. Perrier. ISCA, 2013. 2460-2464.ISSN
1990-9772Funded by
This work was supported by the National Natural Science Foundation of China under Grant No. 61271389 and the National Basic Research Program (973 Program) of China under Grant No. 2013CB329302.Editor's Version
http://www.isca-speech.org/archive/interspeech_2013/i13_2460.htmlSubjects
MAP; fMAPLR; Sequential adaptation; Speaker verification; Informática; TelecomunicacionesRights
© 2013 ISCAAbstract
GMM-UBM-based speaker verification heavily relies on
well-trained UBMs. In practice, it is not often easy to obtain
a UBM that fully matches the acoustic channel in operation. In
a previous study, we proposed to address this problem by a novel
sequential UBM adaptation approach based on MAP. This
work extends the study by applying the sequential approach to
speaker model adaptation. In addition, we investigate a new
feature-space sequential adaptation approach based on feature
MAP linear regression (fMAPLR) and compare it with the previously
proposed model-space MAP approach. We find that
these two approaches are complementary and can be combined
to deliver additional performance gains. The experiments conducted
on a time-varying speech database demonstrate that the
proposed MAP-fMAPLR approach leads to significant EER reduction
with two mismatched UBMs (25% and 39% respectively).
Files in this item
Google Scholar:Wang, Jun
-
Wang, Dong
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Wu, Xiaojun
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Fang Zheng, Thomas
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Tejedor Noguerales, Javier
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