The Effectiveness of the Glottal to Noise Excitation Ratio for the Screening of Voice Disorders

Godino-Llorente, Juan Ignacio; Osma-Ruiz, Víctor; Sáenz-Lechón, Nicolás; Gómez-Vilda, Pedro; Blanco–Velasco, Manuel; Cruz–Roldán, Fernando

doi:10.1016/j.jvoice.2008.04.006

Cited by 74 publications

(63 citation statements)

References 23 publications

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“…In general, jitter and shimmer are used to describe hoarseness found at perceptual evaluation and vibratory irregularity to the physiological extent, whereas noise measurements are used as indicators of air escape and inadequate glottal closure (6,18,(23)(24)(25)(26)(27) . A study reported auditory parameters for hoarseness to be related to shimmer (28) .…”

Section: Discussionmentioning

confidence: 99%

Severity of voice disorders: integration of perceptual and acoustic data in dysphonic patients

Lopes

Cavalcante

Costa

2014

CoDAS

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DescritoresThe vocal deviation from the vowel /ε/ was evaluated by consensus between three speech language pathologists, using a Visual Analog Scale. We extracted the mean and standard deviation (SD) of the fundamental frequency (F 0 ), jitter, shimmer, and Glottal-to-Noise Excitation Ratio (GNE). Results: Mean F 0 was negatively correlated with the degree of roughness and positively with the degree of tension. The F 0 SD was positively correlated with the overall, roughness, tension, and instability grades. The jitter and shimmer were positively correlated with all perceptual parameters. Only the GNE distinguished between healthy and dysphonic voices and rated the degree of breathiness. The shimmer separated rough voices from not rough voices. The mean F 0 was helpful to determine the degree of phonatory tension and to separate rough voices from breathy and strained voices. Conclusion:There is a correlation between the acoustic and auditory perceptual measures. The shimmer, the GNE, and F 0 SD can be used to detect roughness, breathing, and strain, respectively. The GNE and mean F 0 are useful to classify the degree of breathiness and strain, respectively. The mean F 0 distinguished between rough, breathy, and strained voices, with rough voices more severe compared to the other two. RESUMO INTRODUCTION Voice is multidimensional(1) and its production is related to anatomic, physiological, emotional, organic, environmental, and behavioral features. Voice evaluation, therefore, must be based on these principles, with mapping of voice production and correlation with such features for one to take a truly comprehensive view of dysphonia.The purpose of voice evaluation is to analyze voice quality, that is, whether the voice is healthy or not; to diagnose voice disorders, to monitor any disease or function progression, to evaluate prognosis, and to identify possible risks to develop a disorder (2) . Overall, studies on voice evaluation and diagnosis have attempted to answer three essential clinical questions (2) : what is the measure to determine presence/absence of any voice disorder (diagnosis)? Is there evidence that the test used in evaluation can determine the nature (etiology) of a voice disorder? What is the measure capacity to determine the severity of a voice disorder?In a study (3) conducted with experienced speech language pathologists in the United States, all the 53 interviewees reported using auditoryperceptual measurements in voice evaluation followed by analysis of body posture and movements, and by dynamic voice evaluation. These subjective methods were substantially more used than objective evaluations by acoustic measurements only.However, in a systematic review, most studies (60%) on voice evaluation in patients with voice disorders used acoustic measurements and focused on presence/absence of a disorder (78%). Few articles (18%) have investigated the ability of a measurement to measure the severity of voice disorder (2) . Also, most studies have used an image of larynx as reference to define the p...

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Section: Discussionmentioning

confidence: 99%

Severity of voice disorders: integration of perceptual and acoustic data in dysphonic patients

Lopes

Cavalcante

Costa

2014

CoDAS

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“…Acoustic analysis employs computerbased techniques to measure the properties of the acoustic signal-whether from a vowel or connected speech-providing more objective information regarding the voice signal compared to the auditoryperceptual analysis, as it produces quantitative data in the voice assessment process [9][10][11][12][13] .…”

Section: Análise Acústica De Vozes Infantis: Contribuições Do Diagrammentioning

confidence: 99%

“…In view of this, some studies [8][9][10][11][12] have indicated that a voice assessment should use a combination of several acoustic measures of perturbation and noise, so that each individual utterance is quantified by a set of attributes.…”

Section: Introductionmentioning

confidence: 99%

Análise acústica de vozes infantis: contribuições do diagrama de desvio fonatório

et al. 2015

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Resumo:OBJETIVO:analisar o poder discriminatório do diagrama de desvio fonatório na avaliação do tipo de voz predominante e da intensidade do desvio vocal em crianças.MÉTODOS:coletou-se a vogal /ε/ sustentada de 93 crianças. A intensidade do desvio e a qualidade vocal foram analisadas por meio da escala analógico-visual. Utilizou-se o diagrama para a análise acústica, com avaliação da distribuição dos sinais vocais de acordo com a área, quadrante, forma e densidade. Realizou-se o teste de igualdade de proporções e o teste Qui-quadrado(x2) para comparar as variáveis, e o teste de Correlação de Spearman para correlacionar as medidas acústicas e perceptivo-auditivas.RESULTADOS: houve correlação entre a classificação de quadrantes e a intensidade do desvio vocal para todos os parâmetros analisados. Houve diferença estatisticamente significante entre a proporção de crianças com rugosidade, soprosidade, tensão e instabilidade em relação à área, ao quadrante e à forma. Não houve diferença estatisticamente significante entre a proporção de crianças com e sem desvio da qualidade vocal em relação a todos os parâmetros analisados, ao considerar a distribuição das vozes nos quadrantes.CONCLUSÃO: o diagrama de desvio fonatório foi capaz de diferenciar a qualidade vocal predominante por meio da distribuição nos quadrantes, embora não tenha discriminado as vozes infantis saudáveis e alteradas.

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“…It includes signal-to-noise ratio (SNR) (Klingholz, 1997), harmonics-to-noise ratio (HNR) (Qi & Hillman, 1997) and (Kasuya, Ogawa, Mashima & Ebihara, 1986), normalized noise energy (NNE) (Michaelis, Gramss & Strube, 1997), voice turbulence index (VTI) (Qi & Hillman, 1997), soft phonation index (SPI) (Deliyski, 1993) and glottal-to-noise excitation ratio (GNE) (Michaelis, Gramss & Strube, 1997) and (Godino-Llorente, Ruiz, Lechon & Gomez-Vilda, 2008).…”

Section: Related Workmentioning

confidence: 99%

“…In (Godino-Llorente, Ruiz, Lechon & Gomez-Vilda, 2008), the authors evaluate the capabilities of the GNE ratio for the screening of voice disorders, reporting an accuracy of 95%. The authors in (Yumoto, Gould & Baer, 1982) proposed the HNR parameter for acoustic discrimination of voice disorders reporting an error rate of 16.7%.…”

Section: Related Workmentioning

confidence: 99%

Computer Aided Recognition of Vocal Folds Disorders by Means of RASTA-PLP

Saudi¹,

Youssif²,

Ghalwash³

2012

CIS

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In the context of the recognition of vocal folds disorders, the systems based on acoustic analysis are being introduced as computer aided medical diagnosis tools due to its objectivity and noninvasive nature. Acoustic analysis is a complementary tool to those methods based on direct observation of the vocal folds by laryngoscopy; also, it can be used for the evaluation of surgical operation. This paper presents a novel approach in voice pathology assessment using RASTA-PLP feature extraction method in the framework of a HMM. The proposed method then compared to other feature extraction methods such as MFCC and PLP. The experimental results show that RASTA-PLP attained 92.86% correct classification rates and AUC of 0.94 compared to 0.81 and 0.79 for MFCC and PLP respectively.

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The Effectiveness of the Glottal to Noise Excitation Ratio for the Screening of Voice Disorders

Cited by 74 publications

References 23 publications

Severity of voice disorders: integration of perceptual and acoustic data in dysphonic patients

Severity of voice disorders: integration of perceptual and acoustic data in dysphonic patients

Análise acústica de vozes infantis: contribuições do diagrama de desvio fonatório

Computer Aided Recognition of Vocal Folds Disorders by Means of RASTA-PLP

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