NDLI: Feature Estimation for Vocal Fold Edema Detection Using Short-Term Cepstral Analysis

Content Provider	IEEE Xplore Digital Library
Author	Aguiar Neto, B.G. Costa, S.C. Fechine, J.M. Muppa, M.
Copyright Year	2007
Description	Author affiliation: Fed. Univ. of Campina Grande, Campina Grande (Aguiar Neto, B.G.)
Abstract	Digital signal processing techniques have been used to perform an acoustic analysis for vocal quality assessment due to the simplicity and the noninvasive nature of the measurement procedures. Their employment is of special interest, as they can provide an objective diagnosis of pathological voices, and may be used as complementary tool in laryngoscope exams. The acoustic modeling of pathological voices is very important to discriminate normal and pathological voices. The degree of reliability and effectiveness of the discriminating process depends on the appropriate acoustic feature extraction. This paper aims at specifying and evaluating the acoustic features for vocal fold edema through a parametric modeling approach based on the resonant structure of the human speech production mechanism, and a nonparametric approach related to human auditory perception system. For this purpose, LPC and LPC-based cepstral coefficients, and mel-frequency cepstral coefficients are used. A vector-quantizing-trained distance classifier is used in the discrimination process.
Starting Page	1158
Ending Page	1162
File Size	961350
Page Count	5
File Format	PDF
ISBN	9781424415090
DOI	10.1109/BIBE.2007.4375707
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cepstral analysis Pathology Humans Acoustic signal detection Digital signal processing Performance evaluation Signal analysis Performance analysis Quality assessment Acoustic measurements speech pathology acoustic voice analysis speech processig cepstral parameters disordered voices
Content Type	Text
Resource Type	Article

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