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Title: Relevance vector machines with empirical likelihood-ratio kernels for PLDA speaker verification
Authors: Rao, W
Mak, MW 
Keywords: Empirical LR kernel
Probabilistic linear discriminant analysis
Relevance vector machines
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014, 2014, 6936591, p. 64-68 How to cite?
Abstract: Previous works have shown the benefits of empirical likelihood ratio (LR) kernels for i-vector/PLDA speaker verification. The method not only utilizes the multiple enrollment utterances of target speakers effectively, but also opens up opportunity for adopting sparse kernel machines for PLDA-based speaker verification systems. This paper proposes taking the advantages of the empirical LR kernels by incorporating them into relevance vector machines (RVMs). Results on NIST 2012 SRE demonstrate that the performance of RVM regression equipped with empirical LR kernels is slightly better than that of the support vector machines after performing utterance partitioning.
Description: 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014, 12-14 September 2014
ISBN: 9781479942206
DOI: 10.1109/ISCSLP.2014.6936591
Appears in Collections:Conference Paper

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