We are pleased to announce that the following papers have been accepted to The Speaker and Language Recognition Workshop (IEEE - Odyssey 2008 -- http://www.speakerodyssey.com).

** Reda Dehak, Najim Dehak, Patrick Kenny, Pierre Dumouchel. Kernel Combination for SVM Speaker Verification.

http://publis.lrde.epita.fr/200709-ODYSSEY-A

We present a new approach for constructing the kernels used to build support vector machines for speaker verification. The idea is to construct new kernels by taking linear combination of many kernels such as the GLDS and GMM supervector kernels. In this new kernel combination, the combination weights are speaker dependent rather than universal weights on score level fusion and there is no need for extra-data to estimate them. An experiment on the NIST 2006 speaker recognition evaluation dataset (all trial) was done using three different kernel functions (GLDS kernel, linear and Gaussian GMM supervector kernels). We compared our kernel combination to the optimal linear score fusion obtained using logistic regression. This optimal score fusion was trained on the same test data. We had an equal error rate of $\simeq 5,9%$ using the kernel combination technique which is better than the optimal score fusion system ($\simeq 6,0%$).

** Reda Dehak, Najim Dehak, Patrick Kenny, Pierre Dumouchel. Comparison Between Factor Analysis and GMM Support Vector Machines for Speaker Verification.

http://publis.lrde.epita.fr/200709-ODYSSEY-B

We present a comparison between speaker verification systems based on factor analysis modeling and support vector machines using GMM supervectors as features. All systems used the same acoustic features and they were trained and tested on the same data sets. We test two types of kernel (one linear, the other non-linear) for the GMM support vector machines. The results show that factor analysis using speaker factors gives the best results on the core condition of the NIST 2006 speaker recognition evaluation. The difference is particularly marked on the English language subset. Fusion of all systems gave an equal error rate of 4.2% (all trials) and 3.2% (English trials only).

** Patrick Kenny, Najim Dehak, Reda Dehak, Vishwa Gupta, Pierre Dumouchel. The Role of Speaker Factors in the NIST Extended Data Task.

http://publis.lrde.epita.fr/200709-ODYSSEY-C

We tested factor analysis models having various numbers of speaker factors on the core condition and the extended data condition of the 2006 NIST speaker recognition evaluation. In order to ensure strict disjointness between training and test sets, the factor analysis models were trained without using any of the data made available for the 2005 evaluation. The factor analysis training set consisted primarily of Switchboard data and so was to some degree mismatched with the 2006 test data (drawn from the Mixer collection). Consequently, our initial results were not as good as those submitted for the 2006 evaluation. However we found that we could compensate for this by a simple modification to our score normalization strategy, namely by using 1000 z-norm utterances in zt-norm.

Our purpose in varying the number of speaker factors was to evaluate the eigenvoiceMAP and classicalMAP components of the inter-speaker variability model in factor analysis. We found that on the core condition (i.e. 2?3 minutes of enrollment data), only the eigenvoice MAP component plays a useful role. On the other hand, on the extended data condition (i.e. 15?20 minutes of enrollment data) both the classical MAP component and the eigenvoice component proved to be useful provided that the number of speaker factors was limited. Our best result on the extended data condition (all trials) was an equal error rate of 2.2% and a detection cost of 0.011.