Designed to safely share the same workspace as humans and assist them in various tasks, the new collaborative robots are targeting manufacturing and service applications that once were considered unattainable. The large diversity of tasks to carry out, the unstructured environments, and the close interaction with humans call for collaborative robots to seamlessly adapt their behaviors, so as to...

There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM’s strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can exte...

Linear discriminant or Karhunen-Lo eve transforms are established techniques for mapping features into a lower dimensional subspace. This paper introduces a uniform statistical framework, where the computation of the optimal feature reduction is formalized as a Maximum-Likelihood estimation problem. The experimental evaluation of this suggested extension of linear selection methods shows a slig...

This paper describes the use of a multiple codebook SCHMM speaker verification system, which uses a novel technique for discriminative hidden Markov modelling known as discriminative observation probabilities (DOP). DOP can easily be added to a multiple codebook HMM system and require minimal additional computation and no additional training. The DOP technique can be applied to both speech and ...

Localization is a difficult problem to be solved because of the demanding requirements of low cost, high energy efficiency, scalability for the network size and the practical issues associated with node deployments. A probabilistic-based approach is used for localization, where the system determines probability of the target that can be located at any point within the environment. After determi...

In this paper we propose a new method for identifying processing stages in human information processing. Since the 1860s scientists have used different methods to identify processing stages, usually based on reaction time (RT) differences between conditions. To overcome the limitations of RT-based methods we used hidden semi-Markov models (HSMMs) to analyze EEG data. This HSMM-EEG methodology c...

This paper describes a style adaptation technique using hidden semi-Markov model (HSMM) based maximum likelihood linear regression (MLLR). The HSMM-based MLLR technique can estimate regression matrices for affine transform of mean vectors of output and state duration distributions which maximize likelihood of adaptation data using EM algorithm. In this study, we apply this adaptation technique ...

Structural information about a document is essential for structured query processing, indexing, and retrieval. A document page can be partitioned into a hierarchy of homogeneous regions such as columns, paragraphs, etc.; these regions are called physical components, and define the physical layout of the page. In this paper we develop a class of models for the physical layouts of technical paper...

We present an evaluation of the perception of foreign-accented natural and synthetic speech in comparison to accent-reduced synthetic speech. Our method for foreign accent conversion is based on mapping of Hidden Semi-Markov Model states between accented and non-accented voice models and does not need an average voice model of accented speech. We employ the method on recorded data of speakers w...