Balancing unique acoustic characteristics of a speaker such as identity and accent, with general acoustic behavior that describes phoneme identity, is one of the great challenges in applying nonparametric Bayesian approaches to speaker adaptation. The Dirichlet Process Mixture (DPM) is a relatively new model that provides an elegant framework in which individual characteristics can be balanced with aggregate behavior without diluting the quality of the individual models. Unlike Gaussian Mixture models (GMMs), which tend to smear multimodal behavior through averaging, the DPM model attempts to preserve unique behaviors through use of an infinite mixture model. In this paper, we present some exploratory research on applying these models to the acoustic modeling component of the speaker adaptation problem. DPM based models are shown to provide up to 10% reduction in WER over maximum likelihood linear regression (MLLR) on a speaker adaptation task based on the Resource Management database.

This paper addresses the problem of piecewise linear approximation of point sets without any constraints on the order of data points or the number of model components (line segments). We point out two problems with the maximum likelihood estimate (MLE) that present serious drawbacks in practical applications. One is that the parametric models obtained using a classical MLE framework are not guaranteed to be close to data points. It is typically impossible, in this classical framework, to detect whether a parametric model fits the data well or not. The second problem is related to accurately choosing the optimal number of model components. We first fit a nonparametric density to the data points and use it to define a neighborhood of the data. Observations inside this neighborhood are deemed informative; those outside the neighborhood are deemed uninformative for our purpose. This provides us with a means to recognize when models fail to properly fit the data. We then obtain maximum likelihood estimates by optimizing the Kullback-Leibler Divergence (KLD) between the nonparametric data density restricted to this neighborhood and a mixture of parametric models. We prove that, under the assumption of a reasonably large sample size, the inferred model components are close to their ground-truth model component counterparts. This holds independently of the initial number of assumed model components or their associated parameters. Moreover, in the proposed approach, we are able to estimate the number of significant model components without any additional computation.

Given two shapes, the correspondence between distinct visual features is the basis for most alignment processes and shape similarity measures. This paper presents an approach introducing particle filters to establish perceptually correct correspondences between point sets representing shapes. Local shape feature descriptors are used to establish correspondence probabilities. The global correspondence structure is calculated using additional constraints based on domain knowledge. Domain knowledge is characterized as prior distributions expressing hypotheses about the global relationships between shapes. These hypotheses are generated during the iterative particle filtering process. Experiments using standard alignment techniques, based on the given correspondence relationships, demonstrate the advantages of this approach.

Merging local maps, acquired by multiple robots, into a global map, (also known as map merging) is one of the important issues faced by virtually all cooperative exploration techniques. We present a novel and simple solution to the problem of map merging by reducing it to the problem of SLAM of a single ¿virtual¿ robot. The individual local maps and their shape information constitute the sensor information for the virtual robot. This approach allows us to adapt the framework of Rao-Blackwellized particle filtering used in SLAM of a single robot for the problem of map merging.

The paper shows how particle filters can be used to establish visually consistent partial correspondences between similar features in unrestricted 2D point sets representing shapes. Given an update rule, the PF system has the advantage that global constraints can be learned. We motivate and define the update rule for the given task and show its superior performance in comparison to a globally unrestricted approach.

We introduce a new interpretation of multiscale random fields (MSRFs) that admits efficient optimization in the framework of regular (single level) random fields (RFs). It is based on a new operator, called append, that combines sets of random variables (RVs) to single RVs. We assume that a MSRF can be decomposed into disjoint trees that link RVs at different pyramid levels. The append operator is then applied to map RVs in each tree structure to a single RV. We demonstrate the usefulness of the proposed approach on a challenging task involving grouping contours of target shapes in images. MSRFs provide a natural representation of multiscale contour models, which are needed in order to cope with unstable contour decompositions. The append operator allows us to find optimal image labels using the classical framework of relaxation labeling, Alternative methods like Markov Chain Monte Carlo (MCMC) could also be used.

We introduce a new EM framework in which it is possible not only to optimize the model parameters but also the number of model components. A key feature of our approach is that we use nonparametric density estimation to improve parametric density estimation in the EM framework. While the classical EM algorithm estimates model parameters empirically using the data points themselves, we estimate them using nonparametric density estimates.There exist many possible applications that require optimal adjustment of model components. We present experimental results in two domains. One is polygonal approximation of laser range data, which is an active research topic in robot navigation. The other is grouping of edge pixels to contour boundaries, which still belongs to unsolved problems in computer vision.

We propose a novel clustering algorithm that is similar in spirit to classification trees. The data is recursively split using a criterion that applies a discrete curve evolution method to the histogram of distances. The algorithm can be depicted through tree diagrams with triple splits. Leaf nodes represent either clusters or sets of observations that can not yet be clearly assigned to a cluster. After constructing the tree, unclassified data points are mapped to their closest clusters. The algorithm has several advantages. First, it deals effectively with observations that can not be unambiguously assigned to a cluster by allowing a "margin of error". Second, it automatically determines the number of clusters; apart from the margin of error the user only needs to specify the minimal cluster size but not the number of clusters. Third, it is linear with respect to the number of data points and thus suitable for very large data sets. Experiments involving both simulated and real data from different domains show that the proposed method is effective and efficient.