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%0 Conference Proceedings
%A Obin, Nicolas
%A Rodet, Xavier
%A Lacheret, Anne
%T French Prominence: A Probabilistic Framework
%D 2008
%B International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
%C Las Vegas
%F Obin08a
%K Prosody
%K prominence
%K acoustic correlates
%K feature selection
%K classification
%K GMMl
%X Identification of prosodic phenomena is of first importance in prosodic analysis and modeling. In this paper, we introduce a new method for automatic prosodic phenomena labelling. The authors set their approach of prosodic phenomena in the framework of prominence. The proposed method for automatic prominence labelling is based on well-known machine learning techniques in a three step procedure: i) a feature extraction step in which we propose a framework for systematic and multi-level speech acoustic feature extraction, ii) a feature selection step for identifying the more relevant prominence acoustic correlates, and iii) a modelling step in which a gaussian mixture model is used for predicting prominence. This model shows robust performance on read speech (84%).
%1 6
%2 1
%U http://architexte.ircam.fr/textes/Obin08a/