  This article investigates into recently emerging approaches that use deep
neural networks for the estimation of glottal closure instants (GCI). We build
upon our previous approach that used synthetic speech exclusively to create
perfectly annotated training data and that had been shown to compare favourably
with other training approaches using electroglottograph (EGG) signals. Here we
introduce a semi-supervised training strategy that allows refining the
estimator by means of an analysis-synthesis setup using real speech signals,
for which GCI ground truth does not exist. Evaluation of the analyser is
performed by means of comparing the GCI extracted from the glottal flow signal
generated by the analyser with the GCI extracted from EGG on the CMU arctic
dataset, where EGG signals were recorded in addition to speech. We observe that
(1.) the artificial increase of the diversity of pulse shapes that has been
used in our previous construction of the synthetic database is beneficial, (2.)
training the GCI network in the analysis-synthesis setup allows achieving a
very significant improvement of the GCI analyser, (3.) additional
regularisation strategies allow improving the final analysis network when
trained in the analysis-synthesis setup.
