In order to collaborate with people in the real world, cognitive systems must be able to represent and reason about spatial regions in human environments. Consider the command “go to the front of the classroom”. The spatial region mentioned (the front of the classroom) is not perceivable using geometry alone. Instead it is defined by its functional use, implied by nearby objects and their confi...

Recently, a Probabilistic Phone Mapping (PPM) model was proposed to facilitate cross-lingual automatic speech recognition using a foreign phonetic system. Under this framework, discrete hidden Markov models (HMMs) are used to map a foreign phone sequence to a target phone sequence. Context-sensitive mapping is made possible by expanding the discrete observation symbols to include the contexts o...

In this paper, we present a robust face alignment system that is capable of dealing with exaggerating expressions, large occlusions, and a wide variety of image noises. The robustness comes from our shape regularization model, which incorporates constrained nonlinear shape prior, geometric transformation, and likelihood of multiple candidate landmarks in a three-layered generative model. The in...

We present an empirical study of the applicability of Probabilistic Lexicalized Tree Insertion Grammars (PLTIG), a lexicalized counterpart to Probabilistic Context-Free Grammars (PCFG), to problems in stochastic naturallanguage processing. Comparing the performance of PLTIGs with non-hierarchicalN -gram models and PCFGs, we show that PLTIG combines the best aspects of both, with language modeli...

One of the most important factors of success in the development of a software product line is the elicitation, management, and representation of variability. Feature models, are used as a key artifact to express requirements variability and are the basis for the domain architecture design. In this context, this article explores the possible advantages of Model Driven Engineering (MDE) and shows...

The majority of speech recognition systems today commonly use Hidden Markov Models (HMMs) as acoustic models in systems since they can powerfully train and map a speech utterance into a sequence of units. Such systems perform even better if the units are context-dependent. Analogously, when HMM techniques are applied to the problem of articulatory feature extraction, contextdependent articulato...

An af®ne-invariant (AI) deformable contour model for object matching, called AI-EigenSnake (AI-ES), is proposed in the Bayesian framework. In AI-ES, the prior distribution of object shapes is estimated from the sample data. This distribution is then used to constrain the prototype contour, which is dynamically adjustable in the matching process. In this way, large shape deformations due to the ...

We present a framework for recognizing isolated and continuous American Sign Language (ASL) sentences from three-dimensional data. The data are obtained by using physics-based three-dimensional tracking methods and then presented as input to Hidden Markov Models (HMMs) for recognition. To improve recognition performance, we model context-dependent HMMs and present a novel method of coupling thr...

This study is a first step in selecting an appropriate subword unit representation to synthesize highly intelligible 3D talking faces. Consonant confusions were obtained with optic features from a 320-sentence database, spoken by a male talker, using Gaussian mixture models and maximum a posteriori classification methods. The results were compared to consonant confusions obtained from visual-on...

The collection and analysis of observational and experimental data represent the main tools for assessing the presence, the extent, the nature, and the trend of discrimination phenomena. Data analysis techniques have been proposed in the last fifty years in the economic, legal, statistical, and, recently, in the data mining literature. This is not surprising, since discrimination analysis is a ...