  Glottal Closure Instants (GCI) detection consists in automatically detecting
temporal locations of most significant excitation of the vocal tract from the
speech signal. It is used in many speech analysis and processing applications,
and various algorithms have been proposed for this purpose. Recently, new
approaches using convolutional neural networks have emerged, with encouraging
results. Following this trend, we propose a simple approach that performs a
mapping from the speech waveform to a target signal from which the GCIs are
obtained by peak-picking. However, the ground truth GCIs used for training and
evaluation are usually extracted from EGG signals, which are not perfectly
reliable and often not available. To overcome this problem, we propose to train
our network on high-quality synthetic speech with perfect ground truth. The
performances of the proposed algorithm are compared with three other
state-of-the-art approaches using publicly available datasets, and the impact
of using controlled synthetic or real speech signals in the training stage is
investigated. The experimental results demonstrate that the proposed method
obtains similar or better results than other state-of-the-art algorithms and
that using large synthetic datasets with many speakers offers a better
generalization ability than using a smaller database of real speech and EGG
signals.
