Prosodic prominence is commonly regarded as the perceptual salience of a linguistic unit relative to its environment. However, we are far from having a consensus on how it is measured subjectively and how it relates to objectively measurable acoustic events or linguistic structures such as lexical stress, prosodic focus, etc. Here we will concentrate mainly on the identification of prominence by means of acoustic parameters and automatic techniques. Considering this topic, some questions are still open in the community: (a) How can we reliably define and portray prosodic prominence? (b) What is the best prominence domain in acoustics? (c) Is prominence a continuous or a discrete phenomenon? (d) What are the acoustic parameters that support it and how can we combine them to reliably identify prominence? (e) To what extent are acoustic parameters language specific? Can we identify universals across languages? (f) What is the best paradigm for the automatic identification of prominence: Rule-Based or Machine Learning Systems? (g) How can we evaluate automatic systems in the right way? This contribution will briefly address these points.

Automatic detection of prosodic prominence by means of acoustic analyses

TAMBURINI, FABIO
2015

Abstract

Prosodic prominence is commonly regarded as the perceptual salience of a linguistic unit relative to its environment. However, we are far from having a consensus on how it is measured subjectively and how it relates to objectively measurable acoustic events or linguistic structures such as lexical stress, prosodic focus, etc. Here we will concentrate mainly on the identification of prominence by means of acoustic parameters and automatic techniques. Considering this topic, some questions are still open in the community: (a) How can we reliably define and portray prosodic prominence? (b) What is the best prominence domain in acoustics? (c) Is prominence a continuous or a discrete phenomenon? (d) What are the acoustic parameters that support it and how can we combine them to reliably identify prominence? (e) To what extent are acoustic parameters language specific? Can we identify universals across languages? (f) What is the best paradigm for the automatic identification of prominence: Rule-Based or Machine Learning Systems? (g) How can we evaluate automatic systems in the right way? This contribution will briefly address these points.
2015
Tamburini, Fabio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/543621
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