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A Syllable-Based Prominence Detection Model Based on Discriminant Analysis and Context-Dependency

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text
 

Genre(s)

article
 

Forme(s)

document numérique
 

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Titre

A Syllable-Based Prominence Detection Model Based on Discriminant Analysis and Context-Dependency
 

Nom(s)

Obin, Nicolas (auteur)
 
Rodet, Xavier (auteur)
 
Lacheret-Dujour, Anne (auteur)
 

Publication

Saint-Pétersbourg, Russie , 2009
 

Description

Résumé

On the basis of our previous work, we propose a syllable-based prominence detection model within the framework of exploratory data analysis and discriminant learning in the acoustic domain. This paper investigates two hypothesis on the acoustic data processing: a linear discriminant analysis in which the relative discriminant ability of single prosodic cues are combined into prosodic patterns and a context-dependant model that accounts for phonological dependencies (phonetic intrinsic properties and coarticulation effect). The proposed approach significantly outperforms a baseline method on a corpus of French read speech with a performance of 87.5% in f-measure for the prominent syllables (respectively 90.4% in global accuracy).
 

Note(s)

Contribution au colloque ou congrès : Speech and Computer
 

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Date de la notice

2011-07-01 02:00:00
 

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