This paper presents a new method for sampling the null space of the acoustic-to-articulatory mapping, which is considerably faster and more accurate than the previous method presented by Ouni and Laprie [4]. This is achieved by using a simple stochastic exploration of the articulatory space instead of complex linear programming techniques. This new method allows for a much faster and more accur...

We describe a grammarless method for simultaneously bracketing both halves of a parallel text and giving word alignments, assuming only a translation lexicon for the language pair. We introduce inversion-invariant transduction grammars which serve as generative models for parallel bilingual sentences with weak order constraints. Focusing on transduction grammars for bracketing, we formulate a n...

We present a biparsing algorithm for Stochastic Bracketing Inversion Transduction Grammars that runs in O(bn3) time instead of O(n6). Transduction grammars learned via an EM estimation procedure based on this biparsing algorithm are evaluated directly on the translation task, by building a phrase-based statistical MT system on top of the alignments dictated by Viterbi parses under the induced b...

We introduce a technique for quantifying and then exploiting uncertainty in nonlinear stochastic control systems. The approach is suboptimal though robust and relies upon the approximation of the forward and inverse plant models by neural networks, which also estimate the intrinsic uncertainty. Sampling from the resulting Gaussian distributions of the inversion based neurocontroller allows us t...

We investigate elements of combinatorics from the point of view of the algebra for the canonical commutation relations. We find novel interpretations for Stirling numbers of both type within this context. This allows us to develop compact formulas for vacuum averages and normal orderings of products of Bose fields. Two applications are mentioned: an uniform estimate for Dyson series expansions ...

Current techniques for training representations of the articulatory-acoustic mapping from data rely on arti cial simulations to provide codebooks of articulatory and acoustic measurements, which are then modelled by simple functional approximations. This paper outlines a stochastic framework for adapting an arti cial model to real speech from acoustic measurements alone, using the EM algorithm....

A new approach for modeling inverse problems using a Bayesian inference method is introduced. The Bayesian approach considers the unknown parameters as random variables and seeks the probabilistic distribution of the unknowns. By introducing the concept of the stochastic prior state space to the Bayesian formulation, we reformulate the deterministic forward problem as a stochastic one. The adap...

Gravity data inversion is one of the important steps in the interpretation of practical gravity data. The inversion result can be obtained by minimization of the Tikhonov objective function. The determination of an optimal regularization parameter is highly important in the gravity data inversion. In this work, an attempt was made to use the active constrain balancing (ACB) method to select the...