This paper deals with several cohort methods for score normalization in speaker verification systems. At first, the reasons for score normalization are provided. Next, the principle of score normalization techniques based on Bayesian theorem are explained. The world, cohort, and unconstraint cohort normalization techniques are presented. A new normalization technique, unconstraint cohort extrap...

Gödel’s second incompleteness theorem forbids to prove, in a given theory U , the consistency of many theories—in particular, of the theory U itself—as well as it forbids to prove the normalization property for these theories, since this property implies their consistency. When we cannot prove in a theory U the consistency of a theory T , we can try to prove a relative consistency theorem, that...

One of the difficulties of training deep neural networks is caused by improper scaling between layers. Scaling issues introduce exploding / gradient problems, and have typically been addressed by careful scale-preserving initialization. We investigate the value of preserving scale, or isometry, beyond the initial weights. We propose two methods of maintaing isometry, one exact and one stochasti...

Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems — BN’s error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN’s usage for training larger models and transferring features to computer v...

Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to tha...

Captured images arise through interaction between objects of interest and illuminating light sources. If the latter are unevenly distributed, or are too strong or too weak, the image can have low contrast either locally or globally, impeding its interpretation and reducing its usefulness. In practice, control of illumination conditions is challenging, and not always possible. Thus, we propose a...

We describe a comprehensive framework for performing rapid and automatic non-label based nonlinear spatial normalizations. The approach adopted minimizes the residual squared diierence between an image and a template of the same modality. In order to reduce the number of parameters to be tted, the nonlinear warps are described by a linear combination of low spatial frequency basis functions. Th...

Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This paper describes normalization methods based on the fact that dye balance typically varies with spot intensity and with spatial position on the array. Print-tip loess normalization provides a wel...