Authors:
Abdenour Hacine-Gharbi
1
;
Philippe Ravier
2
and
François Nemo
2
Affiliations:
1
University of Bordj Bou Arréridj, Algeria
;
2
University of Orleans, France
Keyword(s):
Prosodic Classification, Local and Global Prosodic Features, Dimensionality Reduction, Curse of Dimensionality, Filter Feature Selection, Mutual Information, Classification of Word’s Uses.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Classification
;
Feature Selection and Extraction
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Symbolic Systems
;
Theory and Methods
Abstract:
The aim of this study is to evaluate the ability of local or global prosodic features in achieving a classification
task of word’s uses. The use of French word “oui” in spontaneous discourse can be identified as belonging to
the class “convinced (CV)”or “lack of conviction (NCV)”. Statistics of classical prosodic patterns are
considered for the classification task. Local features are those computed on single phonemes. Global features
are computed on the whole word. The results show that 10 features completely explain the two clusters CV
and NCV carried out by linguistic experts, the features having being selected thanks to the Max-Relevance
Min-Redundancy filter selection strategy. The duration of the phoneme /w/ is found to be highly relevant for
all the investigated classification systems. Local features are predominantly more relevant than global ones.
The system was validated by building classification systems in a speaker dependent mode and in a speaker
independent mode and also
by investigating manual phoneme segmentation and automatic phoneme
segmentation. In the most favorable case (speaker dependent mode and manual phoneme segmentation), the
rate reached 87.72%. The classification rate reached 78.57% in the speaker independent mode with automatic
phoneme segmentation which is a system configuration close to an industrial one.
(More)