The paper considers text independent speaker identification over the telephone using short training and testing data. Gaussian Mixture Modeling (GMM) is used in the testing phase, but the parameters of the model are taken from clusters obtained for the training data by an adequate choice of feature vectors and a distance measure without optimization in the maximum likelihood (ML) sense. This di...

In this paper we present a non-traditional approach to the problem of estimating the parameters of a stochastic linear system. The method is based on the ExpectationMaximization algorithm and can be considered as the continuous analog of the BaumWelch estimation algorithm for hidden Markov models. We use the algorithm for training the parameters of a dynamical system model that we propose for b...

Trees provide a suited structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, or conversion of tree structured documents. In this context, many applications require the calculation of similarities between tree pairs. The most studied distance is likely the tree edit distance (ED) for which improvements in terms of complexity ...

Model-based clustering techniques have been widely used and have shown promising results in many applications involving complex data. This paper presents a unified framework for probabilistic model-based clustering based on a bipartite graph view of data and models that highlights the commonalities and differences among existing model-based clustering algorithms. In this view, clusters are repr...

A wide variety of computer vision applications rely on superpixel or supervoxel algorithms as a preprocessing step. This underlines the overall importance that these algorithms have gained in the recent years. However, most methods show a lack of temporal consistency or fail in producing temporally stable segmentations. In this paper, we propose a novel, contour-based approach that generates te...

We present nonlinear state-space models (NSSMs) as a general method for the probabilistic modelling of sequences and time-series. NSSMs take the form of iterated maps on continuous state-spaces, and can have either discrete or continuous valued output functions. They are generalizations of the more well known state-space models such as Hidden Markov models (HMMs), and Kalman Filter Models (KFMs...

This paper proposes an unsupervised learning algorithm for Optimality Theoretic grammars, which learns a complete constraint ranking and a lexicon given only unstructured surface forms and morphological relations. The learning algorithm, which is based on the ExpectationMaximization algorithm, gradually maximizes the likelihood of the observed forms by adjusting the parameters of a probabilisti...

This paper proposes a robust visual Simultaneous Localization and Mapping(vSLAM) method that consists of two parts: (1)an initialization method for a 3D map and camera poses, and (2)an outlier rejection method for moving objects. We introduce weighted tentative initial values of a camera pose and 3-D map to reduce the user operation load. We also distinguish outliers of feature points between m...

We study a class of overrelaxed bound optimization algorithms, and their relationship to standard bound optimizers, such as ExpectationMaximization, Iterative Scaling, CCCP and Non-Negative Matrix Factorization. We provide a theoretical analysis of the convergence properties of these optimizers and identify analytic conditions under which they are expected to outperform the standard versions. B...

The performance of speaker recognition algorithms drops signi cantly when testing and training acoustic environments di er. This decrease is caused by the statistical mismatch between the statistics representing the speaker and the testing acoustic data. This paper reports our preliminary results on the application of a novel environmental compensation algorithm to the problem of speaker recogn...