A Hidden Markov Model (HMM) Toolbox within the Matlab environment is presented. In this toolbox, the conventional techniques for the continuous and discrete HMM are developed for the training as well as for the test phases. The ability to make different groups of components for the vector pattern is provided. Multilabeling techniques for the discrete HMM is also provided. The toolbox includes p...

The hidden Markov model (HMM), used with Gaussian Process (GP) as an emission model, has been widely to sequential data in complex form. This study introduces the hybrid Bayesian HMM GP using SM kernel (HMM-GPSM) estimate state of each time-series observation, that is, sequentially observed from a single channel. We then propose scalable inference method train HMM-GPSM large-scale sequences dat...

We propose a new class of hidden Markov model (HMM) called asynchronous-transition HMM (AT-HMM). Opposed to conventional HMMs where hidden state transition occurs simultaneously to all features, the new class of HMM allows state transitions asynchronized between individual features to better model asynchronous timings of acoustic feature changes. In this paper, we focus on a particular class of...

Lane-level map matching is essential for autonomous driving. In this paper, we propose a Hidden Markov Model (HMM) trajectory of noisy GPS measurements to the road lanes in which vehicle records its positions. To our knowledge, first time that HMM used lane-level matching. Apart from values, model further assisted by yaw rate data (converted lane change indicator signal) and visual cues form le...

The simulation of real systems involves the building and analysis of models. Various types of discrete stochastic models (DSM) are well suitable to represent real processes with time dependent behavior, however to build a DSM, the system has to be completely observable. Hidden Markov Models (HMM) can model and analyze systems which are only observable through their interaction with the environm...

The embedded hidden Markov model (HMM) is a statistical model that can be used in many pattern recognition and computer vision applications. This model inherits the partial size invariance of the standard HMM, and, due to its pseudo two-dimensional structure, is able to model twodimensional data such as images, better than the standard HMM. In this paper we describe the maximum likelihood train...

This paper presents an adaptive algorithm for compensating pronunciation variations in hidden Markov model (HMM) based speech recognition. The proposed method aims to adapt the HMM topology and the corresponding HMM parameters to meet the variations of speaker dialects. In adaptive HMM topology, two hypothesis test schemes are designed to detect whether a new speaking variation occurs in state/...

In this paper, a new method of 3D model automatic annotation is proposed based on a twodimensional Hidden Markov Model (2-D HMM). Growing importance in the last years Hidden Markov Models are a widely used methodology for sequential data modeling. Recent years, HMMs are applied to research of automatic annotation, such as images and models annotation. The three basic problems with HMM-liked mod...

Conventional block-based image classiication algorithms, such as CART and VQ based clas-siication, ignore the statistical dependency among image blocks. Consequently, these algorithms often suuer from over-localization. In order to beneet from the inter-block dependency, an image classiication algorithm based on a hidden Markov model (HMM) is developed. An HMM for image classiication, a two dim...