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Effect of Recognition Errors on Text Clustering

Publications associées (34)

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Robust audio segmentation

Jitendra Ajmera

Audio segmentation, in general, is the task of segmenting a continuous audio stream in terms of acoustically homogenous regions, where the rule of homogeneity depends on the task. This thesis aims at developing and investigating efficient, robust and unsup ...

EPFL2005

Improving Speech Recognition Using a Data-Driven Approach

Hervé Bourlard, Guillermo Aradilla

In this paper, we investigate the possibility of enhancing state-of-the-art HMM-based speech recognition systems using data-driven techniques, where whole set of training utterances is used as reference models and recognition is then performed through the ...

IDIAP2005

Improving Speech Recognition Using a Data-Driven Approach

Hervé Bourlard, Guillermo Aradilla

2005

Kernelized Infomax Clustering

We propose a simple information-theoretic clustering approach based on maximizing the mutual information

I(\sfx,y)

between the unknown cluster labels

y

and the training patterns

\sfx

with respect to parameters of specifically constrained encoding dis ...

IDIAP2005

Noisy Text Clustering

David Grangier, Alessandro Vinciarelli

This work presents document clustering experiments performed over noisy texts (i.e. text that have been extracted through an automatic process like speech or character recognition). The effect of recognition errors on different clustering techniques is mea ...

IDIAP2004

Robust Audio Segmentation

Hervé Bourlard, Jitendra Ajmera

IDIAP2004

Robust Audio Segmentation

Hervé Bourlard, Jitendra Ajmera

École Polytechnique Fédérale de Lausanne2004

Making Retrieval Faster Through Document Clustering

David Grangier, Alessandro Vinciarelli

This work addresses the problem of reducing the time between query submission and results output in a retrieval system. The goal is achieved by considering only a database fraction as small as possible during the retrieval process. Our approach is based on ...

IDIAP2004

A clustering technique for the identification of piecewise affine and hybrid systems

Giancarlo Ferrari Trecate

We propose a new technique for the identification of discrete-time hybrid systems in the Piece-Wise Affine (PWA) form. This problem can be formulated as the reconstruction of a possibly discontinuous PWA map with a multi-dimensional domain. In order to ach ...

2003

Dichotomy Between Clustering Performance and Minimum Distortion in Piecewise-Dependent-Data (PDD) Clustering

In many signal such speech, bio-signals, protein chains, etc. there is a dependency between consecutive vectors. As the dependency is limited in duration such data can be called as Piecewise-Dependent- Data (PDD). In clustering it is frequently needed to m ...

2003

Kernelized Infomax Clustering

We propose a simple information-theoretic clustering approach based on maximizing the mutual information

I(\sfx,y)

between the unknown cluster labels

y

and the training patterns

\sfx

with respect to parameters of specifically constrained encoding dis ...

IDIAP2005