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	title        = {Modelling prosodic structure using Artificial Neural Networks},
	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.},
	booktitle    = {ExLing 2017. Proceedings of 8 th Tutorial and Research Workshop on Experimental Linguistics, 19-22 June 2017, Heraklion, Crete, Greece  / edited by Antonis Botinis },
	author       = {Bernardy, Jean-Philippe and Themistocleous, Charalambos},
	year         = {2017},
	publisher    = {University of Athens},
	address      = {Athens},
	ISBN         = {978-960-466-162-6},