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      An Investigation of Vocal Tract Characteristics for Acoustic Discrimination of Pathological Voices

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          Abstract

          This paper investigates the effectiveness of measures related to vocal tract characteristics in classifying normal and pathological speech. Unlike conventional approaches that mainly focus on features related to the vocal source, vocal tract characteristics are examined to determine if interaction effects between vocal folds and the vocal tract can be used to detect pathological speech. Especially, this paper examines features related to formant frequencies to see if vocal tract characteristics are affected by the nature of the vocal fold-related pathology. To test this hypothesis, stationary fragments of vowel /aa/ produced by 223 normal subjects, 472 vocal fold polyp subjects, and 195 unilateral vocal cord paralysis subjects are analyzed. Based on the acoustic-articulatory relationships, phonation for pathological subjects is found to be associated with measures correlated with a raised tongue body or an advanced tongue root. Vocal tract-related features are also found to be statistically significant from the Kruskal-Wallis test in distinguishing normal and pathological speech. Classification results demonstrate that combining the formant measurements with vocal fold-related features results in improved performance in differentiating vocal pathologies including vocal polyps and unilateral vocal cord paralysis, which suggests that measures related to vocal tract characteristics may provide additional information in diagnosing vocal disorders.

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          Most cited references 36

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          Efficient vector quantization of LPC parameters at 24 bits/frame

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            A cepstrum-based technique for determining a harmonics-to-noise ratio in speech signals.

             G de Krom (1993)
            A new method to calculate a spectral harmonics-to-noise ratio (HNR) in speech signals is presented. The method involves discrimination between harmonic and noise energy in the magnitude spectrum by means of a comb-liftering operation in the cepstrum domain. Sensitivity of HNR to (a) additive noise and (b) jitter was tested with synthetic vowel-like signals, generated at 10 fundamental frequencies. All jitter and noise signals were analyzed at three window lengths in order to investigate the effect of the length of the analysis frame on the estimated HNR values. Results of a multiple linear regression analysis with noise or jitter, F0, and window length as predictors for HNR indicate a major effect of both noise and jitter on HNR, in that HNR decreases almost linearly with increasing noise levels or increasing jitter. The influence of F0 and window length on HNR is small for the jittered signals, but HNR increases considerably with increasing F0 or window length for the noise signals. We conclude that the method seems to be a valid technique for determining the amount of spectral noise, because it is almost linearly sensitive to both noise and jitter for a large part of the noise or jitter continuum. The strong negative relation between HNR and jitter illustrates that spectral noise measures cannot simply be taken as indicators of the actual amount of noise in the time signal. Instead, HNR integrates several aspects of the acoustic stability of the signal. As such, HNR may be a useful parameter in the analysis of voice quality, although it cannot be directly interpreted in terms of underlying glottal events or perceptual characteristics.
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              Identification of pathological voices using glottal noise measures.

              We investigated the abilities of four fundamental frequency (F0)-dependent and two F0-independent measures to quantify vocal noise. Two of the F0-dependent measures were computed in the time domain, and two were computed using spectral information from the vowel. The F0-independent measures were based on the linear prediction (LP) modeling of vowel samples. Tests using a database of sustained vowel samples, collected from 53 normal and 175 pathological talkers, showed that measures based on the LP model were much superior to the other measures. A classification rate of 96.5% was achieved by a parameter that quantifies the spectral flatness of the unmodeled component of the vowel sample.
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                Author and article information

                Journal
                Biomed Res Int
                Biomed Res Int
                BMRI
                BioMed Research International
                Hindawi Publishing Corporation
                2314-6133
                2314-6141
                2013
                31 October 2013
                : 2013
                Affiliations
                1Department of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea
                2Research Laboratory of Electronics, Massachusetts Institute of Technology, 50 Vassar Street, Cambridge, MA 02139, USA
                3Department of Otorhinolaryngology—Head and Neck Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Irwon-dong, Gangnam-gu, Seoul 137–710, Republic of Korea
                Author notes

                Academic Editor: Tosiaki Miyati

                Article
                10.1155/2013/758731
                3832970
                Copyright © 2013 Jung-Won Lee et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Research Article

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