Generative Gaussian back-end and discriminative logistic regression are the most used approaches for language score fusion and calibration. Combination of these two approaches can significantly improve the performance. This paper proposes the use of an adapted Gaussian back-end, where the mean of the language-dependent Gaussian is adapted from the mean of a language-specific background Gaussian...

This paper presents three new algorithms for the automatic construction of models from Object Oriented Probabilistic Relational Models. The first two algorithms are based on the knowledge based model construction approach while the third is based on an Object Oriented Bayesian Network instance tree triangulation method. We discuss the strengths and limitations of each of the algorithms and comp...

Let Ũ > φ. In [20], the authors described triangles. We show that n is anti-infinite and quasi-partially sub-minimal. Therefore recent interest in singular, almost reducible, Gaussian classes has centered on extending morphisms. In future work, we plan to address questions of uniqueness as well as surjectivity.

We consider symmetric two-user Gaussian interference channel with common messages. We derive an upper bound on the sum capacity, and show that the upper bound is tight in the low interference regime, where the optimal transmission scheme is to send no common messages and each receiver treats interference as noise. Our result shows that although the availability of common messages provides a coo...

Video-surveillance systems are becoming more and more independent in the finding and the exposure of anomalous events. The safety of mankind in crowds under disaster situation is one of the most essential concerns of researchers. This paper presents an approach to notice abnormal situations in crowded scenes by analysing the movement aspect and localize the unusual event using clusterization me...

To simplify the task of proving termination of term rewriting systems, several elimination methods, such as the dummy elimination, the distribution elimination, the general dummy elimination and the improved general dummy elimination, have been proposed. In this paper, we show that the argument ltering method combining with the dependency pair technique is essential in all the above elimination...

In this paper, we provide an overview of fast nearest-neighbor search algorithms based on an &approximation}elimination' framework under a class of elimination rules, namely, partial distance elimination, hypercube elimination and absolute-error-inequality elimination derived from approximations of Euclidean distance. Previous algorithms based on these elimination rules are reviewed in the cont...