Information Services banner Edinburgh Research Archive The University of Edinburgh crest

Edinburgh Research Archive >
Centre for Speech Technology Research >
CSTR publications >

Please use this identifier to cite or link to this item:

This item has been viewed 110 times in the last year. View Statistics

Files in This Item:

File Description SizeFormat
sap05-svm.pdf490.43 kBAdobe PDFView/Open MBGzipped PostScriptView/Open
Title: Speaker verification using sequence discriminant support vector machines
Authors: Wan, Vincent
Renals, Steve
Issue Date: 2005
Citation: IEEE Trans. on Speech and Audio Processing, 13:203-210, 2005.
Publisher: IEEE Signal Processing Society Press
Abstract: This paper presents a text-independent speaker verification system using support vector machines (SVMs) with score-space kernels. Score-space kernels, generalize Fisher kernels, and are based on an underlying generative model, such as a Gaussian mixture model (GMM). This approach provides direct discrimination between whole sequences, in contrast to the frame-level approaches at the heart of most current systems. The resultant SVMs have a very high dimensionality, since it is related to the number of parameters in the underlying generative model. To ameliorate problems that can arise in the resultant optimization, we introduce a technique called spherical normalization that preconditions the Hessian matrix. We have performed speaker verification experiments using the PolyVar database. The SVM system presented here reduces the relative error rates by 34% compared to a GMM likelihood ratio system.
Keywords: Score-space kernels
Fisher kernels
Gaussian mixture model
Appears in Collections:CSTR publications

Items in ERA are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0! Unless explicitly stated otherwise, all material is copyright © The University of Edinburgh 2013, and/or the original authors. Privacy and Cookies Policy