We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in time, and for which the measurement error is negligible. These conditions are often met, in particular for data related to terrestrial animals, ...

a profile hidden markov model (phmm) is widely used in assigning protein sequences to protein families. in this model, the hidden states only depend on the previous hidden state and observations are independent given hidden states. in other words, in the phmm, only the information of the left side of a hidden state is considered. however, it makes sense that considering the information of the b...

In this paper, we investigate the use of hidden semi-Markov models (HSMMs) in analyzing data of human activities, a task commonly referred to as activity recognition. In particular, we use the models to recognize normal and abnormal twodimensional joystick-generated movements of a cursor, controlled by human users in a computerized clinical maze task. This task – as many other activity recognit...

A semi-continuous hidden Markov model based on the muluple vector quantization codebooks is used here for large.vocabulary speaker-independent continuous speech recognition in the techn,ques employed here. the semi-continuous output probab~hty densHy function for each codebook is represented by a comhinat,on of the corre,~ponding discrete output probablhttes of the hidden Markov model end the c...

Several types of Semi-Continuous HMM (SC-HMM) have been compared with the Continuous Density HMM (CD-HMM) in the context of Speaker Independent Isolated Words Recognition (SI-IWR). It is demonstrated that for the ten-digit vocabulary (TIDIGITS), the SC-HMM outperforms the CD-HMM when memory constraints are imposed on the system. SC-HMMs demonstrate recognition rate of about 95% with a total of ...

There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDPHMM) 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 exten...

An offline recognition system for Arabic handwritten words is presented. The recognition system is based on a semi-continuous 1-dimensional HMM. From each binary word image normalization parameters were estimated. First height, length, and baseline skew are normalized, then features are collected using a sliding window approach. This paper presents these methods in more detail. Some parameters ...

We introduce the minimal maximally predictive models ( -machines) of processes generated by certain hidden semi-Markov models. Their causal states are either hybrid discrete-continuous or continuous random variables and causal-state transitions are described by partial differential equations. Closed-form expressions are given for statistical complexities, excess entropies, and differential info...