GCI identification from voiced speech using the eigen value decomposition of Hankel matrix

In this paper, we present a novel method for robust and accurate identification of glottal closure instants (GCIs) from the voiced speech signal. The proposed method employs a new iterative algorithm based on the eigen value decomposition (EVD) of Hankel matrix to extract the time-varying fundamental frequency (F₀) component of the voiced speech signal. The extracted F₀ component is used to isolated the peak negative cycles of the low frequency range (LFR) filtered voiced speech signal. The GCIs are identified by detecting local minimas in the derivative of falling edges of peak negative cycles of the LFR filtered voiced speech signal which is followed by a selection criterion. The experimental results on speech signals under the white noise environment at various levels of degradation demonstrate that the proposed method outperforms existing methods in terms of accuracy and identification rate.