- 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.
- Links To Paper
- 1st Link
- 2nd Link
- Bibtex format
- @Article{EDI-INF-RR-0660,
- author = {
Steve Renals
and Vincent Wan
},
- title = {Speaker verification using sequence discriminant support vector machines},
- journal = {IEEE Transactions on Speech and Audio Processing},
- publisher = {IEEE Signal Processing Society},
- year = 2005,
- month = {Mar},
- volume = {# 13(2)},
- pages = {203-210},
- doi = {10.1109/TSA.2004.841042},
- url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=30367&arnumber=1395965&count=15&index=5},
- }
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