  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.
