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 fundamenta...

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Bibliographic Details
Published in2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA) pp. 371 - 376
Main Authors Jain, Pooja, Pachori, Ram Bilas
Format Conference Proceeding
LanguageEnglish
Published University of Trieste and University of Zagreb 01.09.2013
Subjects
Eigen Value Decomposition
Glottal Closure Instants
Hankel Matrix
Harmonic analysis
Iterative methods
Noise
Noise measurement
Speech
Speech processing
Speech Signal Processing
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Summary: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 (F 0 ) component of the voiced speech signal. The extracted F 0 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.
ISSN:1845-5921
DOI:10.1109/ISPA.2013.6703769