This work investigates subspace non-parametric models for the task of learning a set acoustic units from unlabeled speech recordings. We constrain base-measure Dirichlet-Process mixture with phonetic subspace---estimated other source languages---to build an \emph{educated prior}, thereby forcing learned to resemble phones known languages. Two types are proposed: (i) Subspace HMM (SHMM) which as...

This paper identifies two novel techniques for face features extraction based on two different multi-resolution analysis tools; the first called curvelet transform while the second is waveatom transform. The resultant features are trained and tested via three improved hidden Markov Model (HMM) classifiers, such as: Structural HMM (SHMM), Deviance Information CriterionInverse Weighted Average K-...

This paper introduces a methodology to recognize handwritten characters using “Structural Hidden Markov Models” (SHMM). The proposed approach is motivated by the need to model complex structures which are encountered in many areas such as speech/handwriting recognition, content-based information retrieval etc. The observations considered are strings that produce the structures. These observatio...

Focusing on short term trend prediction in a financial context, we consider the problem of selective prediction whereby the predictor can abstain from prediction in order to improve performance. We examine two types of selective mechanisms for HMM predictors. The first is a rejection in the spirit of Chow’s well-known ambiguity principle. The second is a specialized mechanism for HMMs that iden...

Building on the current understanding of neural architecture of the visual cortex, we present a graphical model for learning and classification of motion patterns in videos. The model is composed of an arbitrary amount of Hidden Markov Models (HMMs) with shared Gaussian mixture models. The novel extension of our model is the use of additional Markov chain, serving as a switch for indicating the...

We study the problem of modeling human mobility from semantic trace data, wherein each GPS record in a trace is associated with a text message that describes the user’s activity. Existing methods fall short in unveiling human movement regularities for such data, because they either do not model the text data at all or suffer from text sparsity severely. We propose SHMM, a multi-modal spherical ...

Array-based analysis of chromatin immunoprecipitation data (ChIP-chip) is a powerful technique for identifying DNA target regions of individual transcription factors. Here, we present three approaches, a standard log-fold-change analysis (LFC), a basic method based on a Hidden Markov Model (HMM), and an extension of the HMM approach to an HMM with scaled transition matrices (SHMM) to incorporat...

Shear and heat milling machines (SHMMs) constitute a recent mechanical approach that holds promise for the generation of amorphous starch. Here, an tapioca starch SHMM method is developed. The provides higher efficient instant way amorphization rice compared to conventional methods as pressure treatment, ball so on. In this study, temperature initial moisture content are varied, respectively, 8...