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Title: Sparse kernel machines with empirical kernel maps for PLDA speaker verification
Authors: Rao, W 
Mak, MW 
Issue Date: Jul-2016
Source: Computer speech and language, July 2016, v. 38, p. 104-121
Abstract: Previous studies have demonstrated the benefits of PLDA-SVM scoring with empirical kernel maps for i-vector/PLDA speaker verification. The method not only performs significantly better than the conventional PLDA scoring and utilizes the multiple enrollment utterances of target speakers effectively, but also opens up opportunity for adopting sparse kernel machines in PLDA-based speaker verification systems. This paper proposes taking the advantages of empirical kernel maps by incorporating them into a more advanced kernel machine called relevance vector machines (RVMs). The paper reports extensive analyses on the behaviors of RVMs and provides insight into the properties of RVMs and their applications in i-vector/PLDA speaker verification. Results on NIST 2012 SRE demonstrate that PLDA-RVM outperforms the conventional PLDA and that it achieves a comparable performance as PLDA-SVM. Results also show that PLDA-RVM is much sparser than PLDA-SVM.
Keywords: Empirical kernel maps
Probabilistic linear discriminant analysis
Relevance vector machines
Publisher: Academic Press
Journal: Computer speech and language 
ISSN: 0885-2308
DOI: 10.1016/j.csl.2016.01.001
Rights: © 2016 Elsevier Ltd. All rights reserved.
© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
The following publication Rao, W., & Mak, M. W. (2016). Sparse kernel machines with empirical kernel maps for PLDA speaker verification. Computer Speech & Language, 38, 104-121 is available at
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