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Publications associées (218)

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Robust Speaker Change Detection

Hervé Bourlard, Jitendra Ajmera

Most commonly used criteria for speaker change detection like log likelihood ratio (LLR) and Bayesian information criterion (BIC) have an adjustathreshold/penalty parameter to make speaker change decisions. These parameters robust to different acoustic con ...

2003

Penalized Maximum Likelihood Estimation for Normal Mixture Distributions

Andrea Ridolfi

Mixture models form the essential basis of data clustering within a statistical framework. Here, the estimation of the parameters of a mixture of Gaussian densities is considered. In this particular context, it is well known that the maximum likelihood app ...

2003

Conditional Gaussian Mixtures

I show how conditional Gaussians, whose means are conditioned by a random variable, can be estimated and their likelihoods computed. This is based upon how regular Gaussians have their own parameters and likelihood computed. After explaining how to estimat ...

IDIAP2003

GA optimization of terminal antennas by the estimation of the population density of probability using dependency trees

Anja Skrivervik, Francisco Jesus Nunez

An application to antenna optimization of bayesian network density of probability estimators is presented. This technique is very usefull for optimizations where abig number of parameters, multiple solutions and local minima increase the likelihood to conv ...

2003

Optimal Hebbian Learning: a Probabilistic Point of View

Wulfram Gerstner, Jean-Pascal Théodor Pfister

Many activity dependent learning rules have been proposed in order to model long-term potentiation (LTP). Our aim is to derive a spike time dependent learning rule from a probabilistic optimality criterion. Our approach allows us to obtain quantitative res ...

Istanbul, Turkey2003

An Implicit Motion Likelihood for Tracking with Particle Filters

Daniel Gatica-Perez, Jean-Marc Odobez, Silèye Oumar Ba

Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the da ...

IDIAP2003

An Implicit Motion Likelihood for Tracking with Particle Filters

Daniel Gatica-Perez, Jean-Marc Odobez, Silèye Oumar Ba

Springer Verlag2003

Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model

Previous work on statistical language modeling has shown that it is possible to train a feed-forward neural network to approximate probabilities over sequences of words, resulting in significant error reduction when compared to standard baseline models. Ho ...

IDIAP2003

On the Combination of Speech and Speaker Recognition

Hervé Bourlard

This paper investigates an approach that maximizes the joint posterior probabil ity of the pronounced word and the speaker identity given the observed data. This probability can be expressed as a product of the posterior probability of the pronounced word ...

IDIAP2003

On the Combination of Speech and Speaker Recognition

Hervé Bourlard

2003