Publications related to Fast high-dimensional Bayesian classification and clustering

Acoustic Models for Posterior Features in Speech Recognition

In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increase ...

Ecole Polytechnique Fédérale de Lausanne2008

Acoustic Models for Posterior Features in Speech Recognition

Guillermo Aradilla

In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increase ...

Idiap2008

A Class Of Mean Field Interaction Models for Computer and Communication Systems

Jean-Yves Le Boudec

We consider models of

N

interacting objects, where the interaction is via a common resource and the distribution of states of all objects. We consider the case where the number of transitions per time slot per object vanishes as

N

grows. We show that, ...

2008

COMBINATION OF AGGLOMERATIVE AND SEQUENTIAL CLUSTERING FOR SPEAKER DIARIZATION

Hervé Bourlard, Fabio Valente, Deepu Vijayasenan

This paper aims at investigating the use of sequential clustering for speaker diarization. Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the a ...

2008

Advanced Data Mining Method for Discovering Regions and Trajectories of Moving Objects: "Ciconia ciconia" Scenario

Stefano Spaccapietra, Claudio Magalhaes Carneiro

Trajectory data is of crucial importance for a vast range of applications involving analysis of moving objects behavior. Unfortunately, the extraction of relevant knowledge from trajectory data is hindered by the lack of semantics and the presence of error ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2008

Learning Cluster Type and Dissimilarity Metric for Each Cluster Using a Set of Possible Cluster Types

Arash Arami

One of the shortcomings of the existing clustering methods is their problems dealing with different shape and size clusters. On the other hand, most of these methods are designed for especial cluster types or have good performance dealing with particular s ...

2007

COMBINATION OF AGGLOMERATIVE AND SEQUENTIAL CLUSTERING FOR SPEAKER DIARIZATION

Hervé Bourlard, Fabio Valente, Deepu Vijayasenan

This paper aims at investigating the use of sequential clustering for speaker diarization. Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the a ...

IDIAP2007

Comparing Different Word Lattice Rescoring Approaches Towards Keyword Spotting

Hervé Bourlard, Hynek Hermansky, Joel Praveen Pinto

In this paper, we further investigate the large vocabulary continuous speech recognition approach to keyword spotting. Given a speech utterance, recognition is performed to obtain a word lattice. The posterior probability of keyword hypotheses in the latti ...

IDIAP2007

A mixture model for multivariate extremes

Anthony Christopher Davison, Marc-Olivier Boldi

The spectral density function plays a key role in ﬁtting the tail of multivariate extremal data and so in estimating probabilities of rare events. This function satisﬁes moment constraints but unlike the univariate extreme value distributions has no simple ...

2007

Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems

Boi Faltings, Vincent Jean Fabrice Schickel

Ontologies are being successfully used to overcome semantic heterogeneity, and are becoming fundamental elements of the Semantic Web. Recently, it has also been shown that ontologies can be used to build more accurate and more personalized recommendation s ...

2007

Fast high-dimensional Bayesian classification and clustering

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Acoustic Models for Posterior Features in Speech Recognition

Acoustic Models for Posterior Features in Speech Recognition

A Class Of Mean Field Interaction Models for Computer and Communication Systems

COMBINATION OF AGGLOMERATIVE AND SEQUENTIAL CLUSTERING FOR SPEAKER DIARIZATION

Advanced Data Mining Method for Discovering Regions and Trajectories of Moving Objects: "Ciconia ciconia" Scenario

Learning Cluster Type and Dissimilarity Metric for Each Cluster Using a Set of Possible Cluster Types

COMBINATION OF AGGLOMERATIVE AND SEQUENTIAL CLUSTERING FOR SPEAKER DIARIZATION

Comparing Different Word Lattice Rescoring Approaches Towards Keyword Spotting

A mixture model for multivariate extremes

Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems

A Class Of Mean Field Interaction Models for Computer and Communication Systems

Acoustic Models for Posterior Features in Speech Recognition

COMBINATION OF AGGLOMERATIVE AND SEQUENTIAL CLUSTERING FOR SPEAKER DIARIZATION

Acoustic Models for Posterior Features in Speech Recognition

COMBINATION OF AGGLOMERATIVE AND SEQUENTIAL CLUSTERING FOR SPEAKER DIARIZATION

Learning Cluster Type and Dissimilarity Metric for Each Cluster Using a Set of Possible Cluster Types

A mixture model for multivariate extremes

Advanced Data Mining Method for Discovering Regions and Trajectories of Moving Objects: "Ciconia ciconia" Scenario

Comparing Different Word Lattice Rescoring Approaches Towards Keyword Spotting

Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems