In this paper a classification-based method for the automatic detection of glottal closure instants (GCIs) from the speech signal is proposed. Peaks in the speech waveforms are taken as candidates for GCI placements. A classification framework is used to train a classification model and to classify whether or not a peak corresponds to the GCI. We show that the detection accuracy in terms of F1 score is 97.27%. In addition, despite using the speech signal only, the proposed method behaves comparably to a method utilizing the glottal signal. The method is also compared with three existing GCI detection algorithms on publicly available databases.
Cite as: Matoušek, J., Tihelka, D. (2017) Classification-Based Detection of Glottal Closure Instants from Speech Signals. Proc. Interspeech 2017, 3053-3057, doi: 10.21437/Interspeech.2017-213
@inproceedings{matousek17_interspeech,
author={Jindřich Matoušek and Daniel Tihelka},
title={{Classification-Based Detection of Glottal Closure Instants from Speech Signals}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={3053--3057},
doi={10.21437/Interspeech.2017-213}
}