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Modelling prosodic structure using Artificial Neural Networks

Conference paper
Authors Jean-Philippe Bernardy
Charalambos Themistocleous
Published in ExLing 2017. Proceedings of 8 th Tutorial and Research Workshop on Experimental Linguistics, 19-22 June 2017, Heraklion, Crete, Greece / edited by Antonis Botinis
ISBN 978-960-466-162-6
Publisher University of Athens
Place of publication Athens
Publication year 2017
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Links exlingworkshop.com/images/ExLing-20...
https://arxiv.org/abs/1706.03952
https://gup.ub.gu.se/file/207081
Keywords Long Short-Term Memory network, convolutional network, prosody, Greek
Subject categories Signal Processing, Phonetics, Languages and Literature

Abstract

The ability to accurately perceive whether a speaker is asking a question or is making a statement is crucial for any successful interaction. However, learning and classifying tonal patterns has been a challenging task for automatic speech recognition and for models of tonal representation, as tonal contours are characterized by significant variation. This paper provides a classification model of Cypriot Greek questions and statements. We evaluate two state-of-the-art network architectures: a Long Short-Term Memory (LSTM) network and a convolutional network (ConvNet). The ConvNet outperforms the LSTM in the classification task and exhibited an excellent performance with 95% classification accuracy.

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