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Speech Communication
Article . 2005 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
DBLP
Article . 2005
Data sources: DBLP
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SFC: A trainable prosodic model

Authors: Gérard Bailly; Bleicke Holm;

SFC: A trainable prosodic model

Abstract

This paper introduces a new model-constrained and data-driven system to generate prosody from metalinguistic information. This system considers the prosodic continuum as the superposition of multiple elementary overlapping multiparametric contours. These contours encode specific metalinguistic functions associated with various discourse units. We describe the phonological model underlying the system and the specific implementation made of that model by the trainable prosodic model described here. The way prosody is analyzed, decomposed and modelled is illustrated by experimental work. In particular, we describe the original training procedure that enables the system to identify the elementary contours and to separate out their contributions to the prosodic contours of the training data.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
40
Top 10%
Top 10%
Average
bronze