This paper describes an implementation of a discriminative acoustical model – a Conditional Random Field (CRF) – within a Dynamic Bayes Net (DBN) formulation of a Hierarchic Hidden Markov Model (HHMM) phone recognizer. This CRF-DBN topology accounts for phone transition dynamics in conditional probability distributions over random variables associated with observed evidence, and therefore has l...

In this study, we investigate a variable-resolution approach to video compression based on Conditional Random Field and statistical conditional sampling in order to further improve compression rate while maintaining high-quality video. In the proposed approach, representative key-frames within a video shot are identified and stored at full resolution. The remaining frames within the video shot ...

Accurate grapheme-to-phoneme (g2p) conversion is needed for several speech processing applications, such as automatic speech synthesis and recognition. For some languages, notably English, improvements of g2p systems are very slow, due to the intricacy of the associations between letter and sounds. In recent years, several improvements have been obtained either by using variable-length associat...

We consider the problem of training discriminative structured output predictors, such as conditional random fields (CRFs) and structured support vector machines (SSVMs). A generalized loss function is introduced, which jointly maximizes the entropy and the margin of the solution. The CRF and SSVM emerge as special cases of our framework. The probabilistic interpretation of large margin methods ...

Kernel canonical correlation analysis (CCA) is a nonlinear extension of CCA, which aims at extracting information shared by two random variables. In this paper, a new notion of conditional kernel CCA is introduced. Conditional kernel CCA aims at analyzing the effect of variable Z to the dependence between X and Y . Rates of convergence of an empirical normalized conditional cross-covariance ope...

We consider the Braching Random Walk of height n and show that the conditional expectation of a gaussian variable at a typical vertex, under positivity, is at least a factor of log n away from the expected maxima.

It is shown that if the conditional densities of a bivariate random variable have maximum entropies, subject to certain constraints, then the bivariate density also maximizes entropy, subject to appropriate constraints. Some examples are discussed.