We present a Switching Kalman Filter Model (SKFM) for the real-time inference of hand kinematics from a population of motor cortical neurons. First we model the probability of the firing rates of the population at a particular time instant as a Gaussian mixture where the mean of each Gaussian is some linear function of the hand kinematics. This mixture contains a “hidden state”, or weight, that...

Abstract. Recently stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. Also image segmentation means to divide one picture into different types of classes or regions, for example a picture of geometric shapes has some classes with different colors such as ’circle’, ’rectangle’, ’triangle’ and so ...

This work presents a probabilistic model for learning robot tasks from human demonstrations using kinesthetic teaching. The difference with respect to previous works is that a complete state of the robot is used to obtain a consistent representation of the dynamics of the task. The learning framework is based on hidden Markov models and Gaussian mixture regression, used for coding and reproduci...

The mixture of Gaussian process functional regressions (GPFRs) assumes that there is a batch time series or sample curves are generated by independent random processes with different temporal structures. However, in real situations, these structures actually transferred manner from long scale. Therefore, the assumption not true practice. In order to get rid this limitation, we propose hidden-Ma...

Hidden Markov models have been used in a wide variety of waveletbased statistical signal processing applications. Typically, Gaussian mixture distributions are used to model the wavelet coefficients and the correlation between the magnitudes of the wavelet coefficients within each scale and/or across the scales is captured by a Markov tree imposed on the (hidden) states of the mixture. This pap...

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...

This paper describes experimental results of applying Subspace Gaussian Mixture Models (SGMMs) in two completely diverse acoustic scenarios: (a) for Large Vocabulary Continuous Speech Recognition (LVCSR) task over (well-resourced) English meeting data and, (b) for acoustic modeling of underresourced Afrikaans telephone data. In both cases, the performance of SGMM models is compared with a conve...

We present a new view of hidden Markov model (HMM) state tying, showing that the accuracy of phonetically tied mixture (PTM) models is similar to, or better than, that of the more typical stateclustered HMM systems. The PTM models require fewer Gaussian distance computations during recognition, and can lead to recognition speedups. We describe a per-phone Gaussian clustering algorithm that auto...

We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In this way, the resulting mixed graphical model, called Markov logic mixtures of Gaussian processes (MLxGP), solves joint Bayesian non-parametric regression and probabilistic relational inference tasks. In turn, MLxGP faci...

Large vocabulary continuous speech recognition is always a difficult task, and it is particularly so for low-resource languages. The scenario we focus on here is having only 1 hour of acoustic training data in the “target” language. This paper presents work on a data borrowing strategy combined with the recently proposed Subspace Gaussian Mixture Model (SGMM). We developed data borrowing strate...