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Stationary Features and Cat Detection

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Using Posterior-Based Features in Template Matching for Speech Recognition

Hervé Bourlard, Guillermo Aradilla

Given the availability of large speech corpora, as well as the increasing of memory and computational resources, the use of template matching approaches for automatic speech recognition (ASR) have recently attracted new attention. In such template-based ap ...

IDIAP2006

Boosting word error rates

Samy Bengio, Christos Dimitrakakis

We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...

2005

Boosting word error rates

Samy Bengio, Christos Dimitrakakis

2005

Gradient estimates of return distributions

Samy Bengio, Christos Dimitrakakis

We present a general method for maintaining estimates of the distribution of parameters in arbitrary models. This is then applied to the estimation of probability distributions over actions in value-based reinforcement learning. While this approach is simi ...

2005

Estimates of Parameter Distributions for Optimal Action Selection

Samy Bengio, Christos Dimitrakakis

We present a general method for maintaining estimates of the distribution of parameters in arbitrary models. This is then applied to the estimation of probability distribution over actions in value-based reinforcement learning. While this approach is simil ...

IDIAP2004

Invariances in Kernel Methods: From Samples to Objects

Samy Bengio, Alexei Pozdnoukhov

This paper presents a general method for incorporating prior knowledge into kernel methods such as Support Vector Machines. It applies when the prior knowledge can be formalized by the description of an object around each sample of the training set, assumi ...

IDIAP2004

Boosting word error rates

Samy Bengio, Christos Dimitrakakis

IDIAP2004

Order Matters: A Distributed Sampling Method for Multi-Object Tracking

Kevin Smith

Multi-Object tracking (MOT) is an important problem in a number of vision applications. For particle filter (PF) tracking, as the number of objects tracked increases, the search space for random sampling explodes in dimension. Partitioned sampling (PS) sol ...

2004

Order Matters: A Distributed Sampling Method for Multi-Object Tracking

Kevin Smith

IDIAP2004

Quantitative $ L ^{ 2 } $ Approximation Error of a Probability Density Estimate Given by It Samples

Michaël Unser, Thierry Blu

We present a new result characterized by an exact integral expression for the approximation error between a probability density and an integer shift invariant estimate obtained from its samples. Unlike the Parzen window estimate, this estimate avoids recom ...

IEEE2004