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

Abstract : 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).
Mots-clés : Linguistique
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https://halshs.archives-ouvertes.fr/halshs-00636518
Contributor : Anne Lacheret-Dujour <>
Submitted on : Friday, November 18, 2011 - 5:34:53 PM
Last modification on : Friday, January 8, 2021 - 2:04:05 PM
Long-term archiving on: : Friday, November 16, 2012 - 11:26:57 AM

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  • HAL Id : halshs-00636518, 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, 2009, Russia. pp.97-100. ⟨halshs-00636518⟩

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