2009
DOI: 10.1159/000227999
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Perturbation Measures of Voice: A Comparative Study between Multi-Dimensional Voice Program and Praat

Abstract: Background/Aims: Frequency and amplitude perturbations are inherent in voice acoustic signals. The assessment of voice perturbation is influenced by several factors, including the type of recording equipment used and the measurement extraction algorithm applied. In the present study, perturbation measures provided by two computer systems (a purpose-built professional voice analysis apparatus and a personal computer-based system for acoustic voice assessment) and two computer programs (Multi-Dimensional Voice P… Show more

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Cited by 77 publications
(44 citation statements)
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“…Surprisingly, our parameters did not change significantly throughout the study period and, similar to perceptual results, no statistically significant difference from pretreatment to study endpoint was observed. This discrepancy between studies may be explained by different voice analysis software being used in different studies [18].…”
Section: Discussionmentioning
confidence: 96%
“…Surprisingly, our parameters did not change significantly throughout the study period and, similar to perceptual results, no statistically significant difference from pretreatment to study endpoint was observed. This discrepancy between studies may be explained by different voice analysis software being used in different studies [18].…”
Section: Discussionmentioning
confidence: 96%
“…Association rules-based similarity between observations x i and x j is computed according to Equation (9):…”
Section: Assessing the Similarity Of Observationsmentioning
confidence: 99%
“…On the other hand, voice signals are obtained non-invasively and computer-based analysis of voice data is used increasingly in monitoring of treatment outcomes and screening for laryngeal disorders [4][5][6][7][8][9][10][11]. Several measures computed from voice data are already widely used to quantify dysphonia changes and characterise outcomes of therapeutic and surgical treatment of laryngeal diseases [8][9][10][11].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…The basic time domain features and MFCCs can be used in developing classifiers models for detection of pathological voices. In the earlier work [11] the neurological disease; PD is considered in which combined features; the time domain and nonlinear dynamic features are given to different classifiers considered independently like SVM, KNearest Neighbor (KNN) and discrimination-function-based (DBF) shows classification accuracy of 91.04%, 93.82% and 82.2% respectively. From the literature it is found that single or elementary classifiers with a single domain or combined features show a lesser classification rate compared to combined features to combined/hybrid classifiers.…”
Section: Introductionmentioning
confidence: 99%