Many interesting human actions involve multiple interacting agents and also have typical durations. Further, there is an inherent hierarchical organization of these activities. In order to model these we introduce a new family of hidden Markov models (HMMs) that provide compositional state representations in both space and time and also a recursive hierarchical structure for inference at higher...

Human activity recognition allows many applications in areas such as intelligent environments and health monitoring. Typically probabilistic models such as the hidden Markov model or conditional random fields are used to map the observed sensor data onto the hidden activity states. A weakness of these models, however, is their inaccurate modelling of state durations. Hidden semi-Markov models a...

gait contains important information about the status of the human body and physiological signs. in many medical applications, it isimportant to monitor and accurately analyze the gait of the patient. since walking shows the reproducibility signs in several phases,separating these phases can be used for the gait analysis. in this study, a method based on image processing for extracting phases of...

Accurately recognizing human activities from sensor data recorded in a smart home setting is a challenging task. Typically, probabilistic models such as the hidden Markov model (HMM) or conditional random fields (CRF) are used to map the observed sensor data onto the hidden activity states. A weakness of these models, however, is that the type of distribution used to model state durations is fi...

There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the traditional HMM. 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 extend the HDP-HMM to capture such structure by drawing upon explicit...

Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Mos...

Tracking the movements of a target based on limited observations plays a role in many interesting applications. Existing probabilistic tracking techniques have shown considerable success but the majority assume simplistic motion models suitable for short-term, local motion prediction. Agent movements are often governed by more sophisticated mechanisms such as a goal-directed pathplanning algori...

Rating how well a routine activity is performed can be valuable in a variety of domains. Making the rating inexpensive and credible is a key aspect of the problem. We formalize the problem as MAP estimation in HMMs where the incoming trace needs repair. We present polynomial time algorithms for computing minimal repairs with maximal likelihood for HMMs, Hidden Semi-Markov Models (HSMMs) and a f...

This paper proposes a technique for controlling singing style in the HMM-based singing voice synthesis. A style control technique based on multiple regression HSMM (MRHSMM), which was originally proposed for the HMM-based expressive speech synthesis, is applied to the conventional technique. The idea of pitch adaptive training is introduced into the MRHSMM to improve the modeling accuracy of fu...