Prosodic prominence, a speech phenomenon by which some linguistic units are perceived as standing out from their environment, plays a very important role in human communication. In this paper we present a study on automatic prominence identification using Probabilistic Graphical Models, a family of Machine Learning Systems able to properly handle sequences of events. We tested the most promising members of such models on utterances selected from a manually annotated Italian speech corpus, obtaining very good recognition results crucially converging with the prominence detection responses provided by a pool of native speakers.
Cite as: Tamburini, F., Bertini, C., Bertinetto, P.M. (2014) Prosodic prominence detection in Italian continuous speech using probabilistic graphical models. Proc. Speech Prosody 2014, 285-289, doi: 10.21437/SpeechProsody.2014-45
@inproceedings{tamburini14_speechprosody,
author={Fabio Tamburini and Chiara Bertini and Pier Marco Bertinetto},
title={{Prosodic prominence detection in Italian continuous speech using probabilistic graphical models}},
year=2014,
booktitle={Proc. Speech Prosody 2014},
pages={285--289},
doi={10.21437/SpeechProsody.2014-45}
}