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Multilingual acoustic modeling for speech recognition based on subspace Gaussian Mixture Models

Publications associées (33)

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The Kaldi Speech Recognition Toolkit

Petr Motlicek

We describe the design of Kaldi, a free, open-source toolkit for speech recognition research. Kaldi provides a speech recognition system based on finite-state transducers (using the freely available OpenFst), together with detailed documentation and script ...

IEEE Signal Processing Society2011

Subspace Gaussian Mixture Models for speech recognition

Pinar Akyazi, Samuel Thomas

We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixture Model structure, and the means and mixture weights vary in a subspace of the total parameter space. We call this a Subspace Gaussian Mixture Model (SGMM) ...

2010

Fast high-dimensional Bayesian classification and clustering

Vahid Partovi Nia

We introduce a fast approach to classification and clustering applicable to high-dimensional continuous data, based on Bayesian mixture models for which explicit computations are available. This permits us to treat classification and clustering in a single ...

EPFL2009

Statistical analysis of clusters of extreme events

Mária Süveges

The thesis is a contribution to extreme-value statistics, more precisely to the estimation of clustering characteristics of extreme values. One summary measure of the tendency to form groups is the inverse average cluster size. In extreme-value context, th ...

EPFL2009

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

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 ...

EPFL2008

Acoustic Models for Posterior Features in Speech Recognition

Guillermo Aradilla

Ecole Polytechnique Fédérale de Lausanne2008

Acoustic Models for Posterior Features in Speech Recognition

Guillermo Aradilla

Idiap2008

Quantifying hydrological modeling errors through a mixture of normal distributions

André Musy, Bettina Schaefli

Bayesian inference of posterior parameter distributions has become widely used in hydrological modeling to estimate the associated modeling uncertainty. The classical underlying statistical model assumes a Gaussian modeling error with zero mean and a given ...

2007

Infinite Models for Speaker Clustering

Fabio Valente

In this paper we propose the use of infinite models for the clustering of speakers. Speaker segmentation is obtained trough a Dirichlet Process Mixture (DPM) model which can be interpreted as a flexible model with an infinite a priori number of components. ...

2006