Principal component analysis of the spectrogram of the speech signal: Interpretation and application to dysarthric speech

Kacha A, Grenez F, Orozco Arroyave JR, Schoentgen J (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Computer Speech and Language

Book Volume: 59

Pages Range: 114-122

DOI: 10.1016/j.csl.2019.07.001

Abstract

The article concerns the interpretation of the principal components of the spectrogram of the speech signal and its application to the description of dysarthric speech. Each principal component is a linear combination of the frame spectra. We show that the first principal component of the spectrogram is closely related to the long-term average spectrum (LTAS) and the second principal component is the difference of two weighted sums of frame spectra reporting open and close vowel frame spectra respectively. We investigate articulation deficits in dysarthric speakers via cues obtained from principal components of the spectrogram of connected speech because long-term average spectra have been claimed to inform about speaker settings of the vocal tract.

Authors with CRIS profile

Juan Rafael Orozco Arroyave Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)

Involved external institutions

Université libre de Bruxelles (ULB) / Free University of Brussels BE Belgium (BE) University of Jijel DZ Algeria (DZ)

How to cite

APA:

Kacha, A., Grenez, F., Orozco Arroyave, J.R., & Schoentgen, J. (2020). Principal component analysis of the spectrogram of the speech signal: Interpretation and application to dysarthric speech. Computer Speech and Language, 59, 114-122. https://doi.org/10.1016/j.csl.2019.07.001

MLA:

Kacha, Abdellah, et al. "Principal component analysis of the spectrogram of the speech signal: Interpretation and application to dysarthric speech." Computer Speech and Language 59 (2020): 114-122.

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