A semi-continuous hidden Markov model based on the multiple vector quantization codebooks is used here for large-vocabulary speaker-independent continuous speech recognition. In the techniques employed here, the semi-continuous output probability density function for each codebook is represented by a combination of the corresponding discrete output probabilities of the hidden Markov model and t...

In this paper we evaluate a method for generating synthetic speech at high speaking rates based on the interpolation of hidden semi-Markov models (HSMMs) trained on speech data recorded at normal and fast speaking rates. The subjective evaluation was carried out with both blind listeners, who are used to very fast speaking rates, and sighted listeners. We show that we can achieve a better intel...

We show how to visually control acoustic speech synthesis by modelling the dependency between visual and acoustic parameters within the Hidden-Semi-Markov-Model (HSMM) based speech synthesis framework. A joint audio-visual model is trained with 3D facial marker trajectories as visual features. Since the dependencies of acoustic features on visual features are only present for certain phones, we...

In this paper we evaluate two different methods for the visual synthesis of Austrian German dialects with parametric HiddenSemi-Markov-Model (HSMM) based speech synthesis. One method uses visual dialect data, i.e. visual dialect recordings that are annotated with dialect phonetic labels, the other methods uses a standard visual model and maps dialect phones to standard phones. This second metho...

In this paper, hidden semi-Markov model (HSMM) is introduced into intrusion detection. Hidden Markov model (HMM) has been applied in intrusion detection systems several years, but it has a major weakness: the inherent duration probability density of a state in HMM is exponential, which may be inappropriate for the modeling of audit data of computer systems. We can handle this problem well by de...

background: routinely collected data from tuberculosis surveillance system can be used to investigate and monitor the irregularities and abrupt changes of the disease incidence. we aimed at using a hidden markov model in order to detect the abnormal states of pulmonary tuberculosis in iran. methods: data for this study were the weekly number of newly diagnosed cases with sputum smear-positive p...

Bayesian models provide powerful tools for analyzing complex time series data, but performing inference with large datasets is a challenge. Stochastic variational inference (SVI) provides a new framework for approximating model posteriors with only a small number of passes through the data, enabling such models to be fit at scale. However, its application to time series models has not been stud...

Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to ex...

We propose a robust estimation algorithm for tracking the location and dynamic motion of a mobile unit in a cellular network. The underlying mobility model is a dynamic linear system driven by a discrete command process that determines the mobile unit’s acceleration. The command process is modeled as a semi-Markov process over a finite set of acceleration levels. Previous approaches to mobility...