Relationship Between Acoustic Measurements and Self-evaluation in Patients With Voice Disorders

Lopes, Leonardo Wanderley; Silva, Jocélio Delfino da; Simões, Layssa Batista; Evangelista, Deyverson da Silva; Silva, Priscila Oliveira Costa; Almeida, Anna Alice; Lima-Silva, Maria Fabiana Bonfim de

doi:10.1016/j.jvoice.2016.02.021

Cited by 29 publications

(13 citation statements)

References 43 publications

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“…In these cases, using self-assessment instruments provides key data to diagnose the presence of dysphonia [55], thereby detecting signals whose voice deviation was not auditorily perceived, albeit with deviant acoustic measurements.…”

Section: Discussionmentioning

confidence: 99%

Performance of Phonatory Deviation Diagrams in Synthesized Voice Analysis

Lopes

Silva

Evangelista

et al. 2017

Folia Phoniatr Logop

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Objective: To analyze the performance of a phonatory deviation diagram (PDD) in discriminating the presence and severity of voice deviation and the predominant voice quality of synthesized voices. Method: A speech-language pathologist performed the auditory-perceptual analysis of the synthesized voice (n = 871). The PDD distribution of voice signals was analyzed according to area, quadrant, shape, and density. Results: Differences in signal distribution regarding the PDD area and quadrant were detected when differentiating the signals with and without voice deviation and with different predominant voice quality. Differences in signal distribution were found in all PDD parameters as a function of the severity of voice disorder. Conclusion: The PDD area and quadrant can differentiate normal voices from deviant synthesized voices. There are differences in signal distribution in PDD area and quadrant as a function of the severity of voice disorder and the predominant voice quality. However, the PDD area and quadrant do not differentiate the signals as a function of severity of voice disorder and differentiated only the breathy and rough voices from the normal and strained voices. PDD density is able to differentiate only signals with moderate and severe deviation. PDD shape shows differences between signals with different severities of voice deviation.

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Section: Discussionmentioning

confidence: 99%

Performance of Phonatory Deviation Diagrams in Synthesized Voice Analysis

Lopes

Silva

Evangelista

et al. 2017

Folia Phoniatr Logop

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“…To design the feature vector, there are two typical approaches: (i) follow clinical and expert guidelines; and (ii) if the first approach is not available, a thorough investigation of the proper features should be carried out. For the voice disorder detection problem, we follow the first approach because there is a clinical acoustic analysis technique available for the formal assessment of voice disorders [26,27]. Four voice quality-based parameters are chosen.…”

Section: Voice Disorder Detection Model Using Ifcmmentioning

confidence: 99%

Combined Generative Adversarial Network and Fuzzy C-Means Clustering for Multi-Class Voice Disorder Detection with an Imbalanced Dataset

2020

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The world has witnessed the success of artificial intelligence deployment for smart healthcare applications. Various studies have suggested that the prevalence of voice disorders in the general population is greater than 10%. An automatic diagnosis for voice disorders via machine learning algorithms is desired to reduce the cost and time needed for examination by doctors and speech-language pathologists. In this paper, a conditional generative adversarial network (CGAN) and improved fuzzy c-means clustering (IFCM) algorithm called CGAN-IFCM is proposed for the multi-class voice disorder detection of three common types of voice disorders. Existing benchmark datasets for voice disorders, the Saarbruecken Voice Database (SVD) and the Voice ICar fEDerico II Database (VOICED), use imbalanced classes. A generative adversarial network offers synthetic data to reduce bias in the detection model. Improved fuzzy c-means clustering considers the relationship between adjacent data points in the fuzzy membership function. To explain the necessity of CGAN and IFCM, a comparison is made between the algorithm with CGAN and that without CGAN. Moreover, the performance is compared between IFCM and traditional fuzzy c-means clustering. Lastly, the proposed CGAN-IFCM outperforms existing models in its true negative rate and true positive rate by 9.9–12.9% and 9.1–44.8%, respectively.

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“…Lopes et al, [5] studied correlation between the acoustic measurements and the VHI score and found that there was no correlation between those measures. Also, Kiagiadaki et al, [30] compared the acoustic measurements, as well as an auditoryperceptual analysis and videostroboscopic examination of the larynx using the VHI with the VoiSS before and after surgery to remove benign laryngeal lesions, and it was observed that there was no correlation between the VHI and other measures carried out in the pre-and postintervention situations.…”

Section: The Assessment Of Dysphonia In Patients With Voice Disordersmentioning

confidence: 99%

Grade of Dysphonia: Correlation with Patient Self-Assessment Questionnaire and Acoustic Measures

Elfiky¹,

Shoeib

2018

The Medical Journal of Cairo University

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Background:The vocal assessment process should consider the multidimensionality involved in the demonstration of a voice disorder. Auditory perceptual assessment is the most commonly used clinical voice assessment method (gold standard) for evaluation of voice disorders. However, it has been heavily criticized because it is subjective and classifies severity of vocal disorders but does not address the impact of the vocal disorder has on quality of life which may go beyond the level of perceived voice change. So, it is possible that self-assessment instruments capture a different aspect of the vocal function that cannot be derived from auditoryperceptual or acoustic analysis. Aim of Study:The aim of this work is to investigate the correlation between grade of dysphonia obtained by auditory perceptual assessment, acoustic measures and the degree of handicap a patient experiences (as measured by the Arabic VHI) as a result of their voice disorder. Study Design:This was a descriptive cross-sectional study.Patients and Methods: 70 adult patients with voice complaints referred for voice evaluation in the Unite of Phoniatrics, Ain Shams University were included in this study aged 18 years-60 years and 35 normal adult subjects as a control group. Auditory perceptual assessment, acoustic analysis, and application of Arabic VHI questionnaire were done for all cases.Results: A significant correlation was found between grade of dysphonia and functional subscale of the Arabic VHI. Significant correlation was found between grade of dysphonia and amplitude perturbation quotient (APQ%) and shimmer percent (Shim%).Significant correlation was found between Arabic VHI total score and average fundamental frequency (F0), significant correlation between functional subscale of the Arabic VHI and average fundamental frequency (F0) and jitter percent (Jitt%) and near significant correlation between physical subscale of the Arabic VHI and average fundamental frequency.Conclusion: Auditory perceptual assessment, acoustic analysis and patient's vocal self-assessment measure different aspects of the voice and are not interchangeable. These measures provide complementary rather than redundant information.

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Relationship Between Acoustic Measurements and Self-evaluation in Patients With Voice Disorders

Cited by 29 publications

References 43 publications

Performance of Phonatory Deviation Diagrams in Synthesized Voice Analysis

Performance of Phonatory Deviation Diagrams in Synthesized Voice Analysis

Combined Generative Adversarial Network and Fuzzy C-Means Clustering for Multi-Class Voice Disorder Detection with an Imbalanced Dataset

Grade of Dysphonia: Correlation with Patient Self-Assessment Questionnaire and Acoustic Measures

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