Classification-Based Detection of Glottal Closure Instants from Speech Signals

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.

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