This paper describes an evaluation of a feature extraction method for visual speech synthesis that is suitable for speaker-adaptive training of a Hidden Semi-Markov Model (HSMM)-based visual speech synthesizer. An audio-visual corpus from three speakers was recorded. While the features used for the auditory modality are well understood, we propose to use a standard Principal Component Analysis ...

Markov switching models (MSMs) are probabilistic models that employ multiple sets of parameters to describe different dynamic regimes that a time series may exhibit at different periods of time. The switching mechanism between regimes is controlled by unobserved random variables that form a first-order Markov chain. Explicit-duration MSMs contain additional variables that explicitly model the d...

a parallel hybrid system of hmm and gmm modeling techniques was implemented and used in a telephony speaker verification and identification system. spectral subtraction and weighted projection measure were used to render this system more robust against additional noise. cepstral mean subtraction method was also applied for the compensation of convolution noise due to transmission channel degrad...

Hidden semi-Markov models (HSMMs) are latent variable models which allow latent state persistence and can be viewed as a generalization of the popular hidden Markov models (HMMs). In this paper, we introduce a novel spectral algorithm to perform inference in HSMMs. Unlike expectation maximization (EM), our approach correctly estimates the probability of given observation sequence based on a set...

Current approaches to off-road autonomous navigation are often limited by their ability to build a terrain model from sensor data. Available sensors make very indirect measurements of quantities of interest such as the supporting ground height and the location of obstacles, especially in domains where vegetation may hide the ground surface or partially obscure obstacles. A generative, probabili...

Health management for a complex nonlinear system is becoming more important for conditionbased maintenance and minimizing the related risks and costs over its entire life. However, a complex nonlinear system often operates under dynamically operational and environmental conditions, and it subjects to high levels of uncertainty and unpredictability so that effective methods for online health man...

This paper addresses the problem of Hidden Markov Models (HMM) training and inference when the training data are composed of feature vectors plus uncertain and imprecise labels. The “soft” labels represent partial knowledge about the possible states at each time step and the “softness” is encoded by belief functions. For the obtained model, called a Partially-Hidden Markov Model (PHMM), the tra...