The goal of this paper is to model the coverbal behavior of a subject involved in face-to-face social interactions. For this end, we present a multimodal behavioral model based on a Dynamic Bayesian Network (DBN). The model was inferred from multimodal data of interacting dyads in a specific scenario designed to foster mutual attention and multimodal deixis of objects and places in a collaborat...

The duration high-order hidden Markov model (DHO-HMM) can capture the dynamic evolution of a physical system more precisely than the first-order hidden Markov model (HMM). The relationship among DHO-HMM, high-order HMM (HO-HMM), hidden semi-Markov model (HSMM), and HMM is presented and discussed. We derived recursive forward and backward probability functions for the partial observation sequenc...

In this paper we investigate external phone duration models (PDMs) for improving the quality of synthetic speech in hidden Markov model (HMM)-based speech synthesis. Support Vector Regression (SVR) and Multilayer Perceptron (MLP) were used for this task. SVR and MLP PDMs were compared with the explicit duration modelling of hidden semi-Markov models (HSMMs). Experiments done on an American Engl...

Distributed Denial of Service (DDoS) attacks have caused continuous critical threats to the Internet services. DDoS attacks are generally conducted at the network layer. Many DDoS attack detection methods are focused on the IP and TCP layers. However, they are not suitable for detecting the application layer DDoS attacks. In this paper, we propose a scheme based on web user browsing behaviors t...

The knowledge of the state sequences that explain a given observed sequence for a known hidden Markovian model is the basis of various methods that may be divided into three categories: (i) enumeration of state sequences; (ii) summary of the possible state sequences in state profiles; (iii) computation of a global measure of the state sequence uncertainty. Concerning the first category, the gen...

Vision based hand gesture recognition systems track the hands and extract their spatial trajectory and shape information, which are then classified with machine learning methods. In this work, we propose a dynamic time warping (DTW) based pre-clustering technique to significantly improve hand gesture recognition accuracy of various graphical models used in the human computer interaction (HCI) l...

In this paper we present a new approach for a generalized tying of mixture components for continuous mixture-density HMM-based speech recognition systems. With an iterative pruning and splitting procedure for the mixture components, this approach ooers a very accurate and detailed representation of the acoustic space and at the same time keeps the number of parameters reasonably small in favor ...

A hidden semi-Markov-switching quantile regression model is introduced as an extension of the Markov-switching one. The proposed allows for arbitrary sojourn-time distributions in states chain. Parameters estimation carried out via maximum likelihood method using Asymmetric Laplace distribution. As a by product specification, formulae and methods forecasting, state prediction, decoding checking...