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Communication dans un congrès Année : 2009

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

Résumé

On the basis of our previous work, we propose a syllablebased 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).
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Dates et versions

halshs-00419977, version 1 (25-09-2009)

Identifiants

  • HAL Id : halshs-00419977 , version 1

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Nicolas Obin, Xavier Rodet, Anne Lacheret-Dujour. A Syllable-Based Prominence Detection Model
Based on Discriminant Analysis and Context-Dependency. Speech and Computer, Jun 2009, Russia. ⟨halshs-00419977⟩
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