In this article, we present a novel approach for continuous operator authentication in teleoperated robotic processes based on Hidden Markov Models (HMM). While HMMs were originally developed and widely used speech recognition, they have shown great performance human motion activity modeling. We make an analogy between language (i.e., words are analogous to teleoperator’s gestures, sentences th...

We present a learning algorithm for hidden Markov models with continuous state and observation spaces. All necessary probability density functions are approximated using samples, along with density trees generated from such samples. A Monte Carlo version of Baum-Welch (EM) is employed to learn models from data, just as in regular HMM learning. Regularization during learning is obtained using an...

Acoustic modeling based on Hidden Markov Models (HMMs) is employed by state-of-theart stochastic speech recognition systems. In continuous density HMMs, the state scores are computed using Gaussian mixture models. On the other hand, Deep Neural Networks (DNN) can be used to compute the HMM state scores. This leads to significant improvement in the recognition accuracy. Conditional Random Fields...

We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective makes it possible to consider novel generalizations of hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usuall...

In this paper, we introduce several hybrid connectionist-structural acoustic models for context-independent phone-like units. The structural part has been modeled with Markov chains or nite state networks learned by grammatical inference techniques. A multilayer perceptron or a committee of multilayer perceptrons is used to estimate the emission probabilities of the structural models. We compar...

The paper aims to identify hidden Markov model parameters. unobservable state represents a finite-state jump process. observations contain Wiener noise with state-dependent intensity. identified parameters include the transition intensity matrix of system state, conditional drift and diffusion coefficients in observations. We propose an iterative identification algorithm based on fixed-interval...

We present an efficient maximum likelihood (ML) training procedure for Gaussian mixture continuous density hidden Markov model (CDHMM) parameters. This procedure is proposed using the concept of approximate prior evolution, posterior intervention and feedback (PEPIF). In a series of experiments for training CDHMMs for a continuous Mandarin Chinese speech recognition task, the new PEPIF procedur...