Multiparameter Voice Assessment in Dysphonics: Correlation Between Objective and Perceptual Parameters

Narasimhan, S. V.; Rashmi, Rajesh

doi:10.1016/j.jvoice.2020.06.009

Cited by 25 publications

(13 citation statements)

References 54 publications

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“…Bhuta et al [ 17 ] found relationships between the voice turbulence index (VTI) and G, NHR with G and R, and soft phonation index (SPI) with G and B. These results were confirmed in more recent papers of Park et al [ 41 ] and Narisimhan et al [ 42 ]. Focusing on SD, Dejonckere et al [ 43 ] found relationships only between B and devoicing measures extracted with AMPEX: PVF (i.e., the proportion of voiced frames), PVS (i.e., the proportion of speech frames), and VL90 (which represents the 90th percentile of the voicing length distribution).…”

Section: Discussionmentioning

confidence: 63%

“…In 25% of observations, NNE, a measure of noise alternative to HNR, was considered relevant, but its total contribution weight was equal to 2.4% only. However, it is important to specify that these discrepancies with literature results [ 39 , 41 , 42 ] are probably caused by different tasks: while jitter and NNE typically show strong correlations with R when healthy subjects are compared with patients diagnosed with AdSD, probably jitter and NNE do not represent relevant parameters to assess and distinguish among different AdSD severity classes. B ratings and F1 median values showed the strongest relationship.…”

Section: Discussionmentioning

confidence: 87%

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Machine Learning Assessment of Spasmodic Dysphonia Based on Acoustical and Perceptual Parameters

et al. 2023

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Adductor spasmodic dysphonia is a type of adult-onset focal dystonia characterized by involuntary spasms of laryngeal muscles. This paper applied machine learning techniques for the severity assessment of spasmodic dysphonia. To this aim, 7 perceptual indices and 48 acoustical parameters were estimated from the Italian word /a’jwɔle/ emitted by 28 female patients, manually segmented from a standardized sentence and used as features in two classification experiments. Subjects were divided into three severity classes (mild, moderate, severe) on the basis of the G (grade) score of the GRB scale. The first aim was that of finding relationships between perceptual and objective measures with the Local Interpretable Model-Agnostic Explanations method. Then, the development of a diagnostic tool for adductor spasmodic dysphonia severity assessment was investigated. Reliable relationships between G; R (Roughness); B (Breathiness); Spasmodicity; and the acoustical parameters: voiced percentage, F2 median, and F1 median were found. After data scaling, Bayesian hyperparameter optimization, and leave-one-out cross-validation, a k-nearest neighbors model provided 89% accuracy in distinguishing patients among the three severity classes. The proposed methods highlighted the best acoustical parameters that could be used jointly with GRB indices to support the perceptual evaluation of spasmodic dysphonia and provide a tool to help severity assessment of spasmodic dysphonia.

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Section: Discussionmentioning

confidence: 63%

Section: Discussionmentioning

confidence: 87%

Machine Learning Assessment of Spasmodic Dysphonia Based on Acoustical and Perceptual Parameters

et al. 2023

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“…In general, voice quality is understood as a multidimensional perceived construct that takes several dimensions into account, for example the grade of any perturbation (overall severity, hoarseness), roughness, breathiness, asthenia and strain. These dimensions correlate approximately with some acoustic measures [3]. Acoustic measures complement auditory-perceptual assessments and enhance the validity of voice diagnostics because they are based on objective measurements and are, therefore, less biased by subjective factors, such as varying intra-rater and inter-rater reliability.…”

Section: Introductionmentioning

confidence: 81%

“…Acoustics plays an important role in the assessment of vocal function in phoniatrics and laryngology, and acoustic measures are recommended metrics in the diagnostic assessment of voice [1]. Acoustic measures are used to evaluate voice functions, such as those reflected in the voice range profile [2] and voice quality [3]. In general, voice quality is understood as a multidimensional perceived construct that takes several dimensions into account, for example the grade of any perturbation (overall severity, hoarseness), roughness, breathiness, asthenia and strain.…”

Section: Introductionmentioning

confidence: 99%

The cepstral spectral index of dysphonia, the acoustic voice quality index and the acoustic breathiness index as novel multiparametric indices for acoustic assessment of voice quality

Latoszek

Mathmann

Neumann

2021

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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Purpose of review The objective assessment of voice quality using acoustic measures is an important pillar of voice diagnostics. This article reviews three recent acoustic measures and their clinical use in phoniatrics and laryngology. Recent findings Two acoustic parameters, the cepstral spectral index of dysphonia (CSID) and the acoustic voice quality index (AVQI), have gained importance as validated multiparametric indices in the objective assessment of hoarseness because they include both continuous speech and sustained vowels. The acoustic breathiness index (ABI), another multiparametric index, assesses breathiness admixture during phonation and identifies it robustly, unaffected by other characteristics of dysphonia such as roughness. Summary Acoustic measurements are useful diagnostic tools when used correctly with an appropriate recording system, consideration of environment and use of software programs. CSID, AVQI and ABI objectively improve the detection of voice quality abnormalities. In addition to their proven validity, their application is simple and their usability for clinicians is high.

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“…In the last decades, numerous acoustic analysis algorithms were developed to measure the pitch, amplitude and waveform perturbation, and spectral and cepstral characteristics of sound waves [ 2 , 5 ]. In order to address the limiting validity of a single acoustic parameter in comparison to the multidimensionality of voice signals, researchers have created several multiparametric acoustic voice indices (MAVIs) during the past few decades.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Analysis of the Multiparametric Acoustic Voice Indices, the VWI, AVQI, ABI, and DSI Measures, in Differentiating between Normal and Dysphonic Voices

Uloza,

Pribuišis,

Ulozaite-Staniene

et al. 2023

JCM

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The study aimed to investigate and compare the accuracy and robustness of the multiparametric acoustic voice indices (MAVIs), namely the Dysphonia Severity Index (DSI), Acoustic Voice Quality Index (AVQI), Acoustic Breathiness Index (ABI), and Voice Wellness Index (VWI) measures in differentiating normal and dysphonic voices. The study group consisted of 129 adult individuals including 49 with normal voices and 80 patients with pathological voices. The diagnostic accuracy of the investigated MAVI in differentiating between normal and pathological voices was assessed using receiver operating characteristics (ROC). Moderate to strong positive linear correlations were observed between different MAVIs. The ROC statistical analysis revealed that all used measurements manifested in a high level of accuracy (area under the curve (AUC) of 0.80 and greater) and an acceptable level of sensitivity and specificity in discriminating between normal and pathological voices. However, with AUC 0.99, the VWI demonstrated the highest diagnostic accuracy. The highest Youden index equaled 0.93, revealing that a VWI cut-off of 4.45 corresponds with highly acceptable sensitivity (97.50%) and specificity (95.92%). In conclusion, the VWI was found to be beneficial in describing differences in voice quality status and discriminating between normal and dysphonic voices based on clinical diagnosis, i.e., dysphonia type, implying the VWI’s reliable voice screening potential.

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Multiparameter Voice Assessment in Dysphonics: Correlation Between Objective and Perceptual Parameters

Cited by 25 publications

References 54 publications

Machine Learning Assessment of Spasmodic Dysphonia Based on Acoustical and Perceptual Parameters

Machine Learning Assessment of Spasmodic Dysphonia Based on Acoustical and Perceptual Parameters

The cepstral spectral index of dysphonia, the acoustic voice quality index and the acoustic breathiness index as novel multiparametric indices for acoustic assessment of voice quality

Accuracy Analysis of the Multiparametric Acoustic Voice Indices, the VWI, AVQI, ABI, and DSI Measures, in Differentiating between Normal and Dysphonic Voices

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