Support Vector Machines and Joint Factor Analysis for Speaker Verification
From LRDE
- Authors
- Najim Dehak, Patrick Kenny, Réda Dehak, Ondrej Glember, Pierre Dumouchel, Lukas Burget, Valiantsina Hubeika, Fabio Castaldo
- Where
- IEEE-ICASSP
- Place
- Taipei - Taiwan
- Type
- inproceedings
- Date
- 2009-04-19
Abstract
This article presents several techniques to combine between Support vector machines (SVM) and Joint Factor Analysis (JFA) model for speaker verification. In this combination, the SVMs are applied on different sources of information produced by the JFA. These informations are the Gaussian Mixture Model supervectors and speakers and Common factors. We found that the use of JFA factors gave the best results especially when within class covariance normalization method is applied in the speaker factors space, in order to compensate for the channel effect. The new combination results are comparable to other classical JFA scoring techniques.
Bibtex (lrde.bib)
@InProceedings{ dehak.09.icassp, author = {Najim Dehak and Patrick Kenny and R\'eda Dehak and Ondrej Glember and Pierre Dumouchel and Lukas Burget and Valiantsina Hubeika and Fabio Castaldo}, title = {Support Vector Machines and Joint Factor Analysis for Speaker Verification}, booktitle = {IEEE-ICASSP}, year = 2009, address = {Taipei - Taiwan}, month = apr, abstract = {This article presents several techniques to combine between Support vector machines (SVM) and Joint Factor Analysis (JFA) model for speaker verification. In this combination, the SVMs are applied on different sources of information produced by the JFA. These informations are the Gaussian Mixture Model supervectors and speakers and Common factors. We found that the use of JFA factors gave the best results especially when within class covariance normalization method is applied in the speaker factors space, in order to compensate for the channel effect. The new combination results are comparable to other classical JFA scoring techniques.} }