Continuous-time state transition models may end up having large unwieldy structures when trying to represent all relevant stages of clinical disease processes by means of a standard Markov model. In such situations, a more parsimonious, and therefore easier-to-grasp, model of a patient's disease progression can often be obtained by assuming that the future state transitions do not depend only o...

Multi-state Markov models have proven useful in many concrete situations and have had successful applications, notably in analysing disease history data. Continuous-time Markov models with three transient states are considered for the study of asthma control evolution in a cohort of 406 patients with persistent asthma. Firstly, the model is considered in the homogeneous case. Covariates are tak...

This paper proposes a boosting algorithm that uses a semi-Markov perceptron. The training algorithm repeats the training of a semi-Markov model and the update of the weights of training samples. In the boosting, training samples that are incorrectly segmented or labeled have large weights. Such training samples are aggressively learned in the training of the semi-Markov perceptron because the w...

This paper presents a technique for controlling intuitively the degree or intensity of speaking styles and emotional expressions of synthetic speech. The conventional style control technique based on multiple regression HMM (MRHMM) has a problem that it is difficult to control phone duration of synthetic speech because HMM has no explicit parameter which models phone duration appropriately. To ...

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...

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...

This article concerns the study of the asymptotic properties of the maximum likelihood estimator (MLE) for the general hidden semi-Markov model (HSMM) with backward recurrence time dependence. By transforming the general HSMM into a general hidden Markov model, we prove that under some regularity conditions, the MLE is strongly consistent and asymptotically normal. We also provide useful expres...