Vocal Fatigue Index in Teachers Using Mokken Analysis

Nanjundeswaran, Chaya D.; Mersbergen, Miriam van; Banks, Russell; Hunter, Eric J.

doi:10.1016/j.jvoice.2020.12.053

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…Future work will involve the assignment of labels on a per-sample basis-as individuals with vocal fatigue, especially those with low vocal fatigue, may still produce 'healthy-like' samples, an issue that was also pointed out for classifying vocal hyperfunction using neck surface acceleration [27]. A limitation of the current dataset is that the majority of cases with elevated VFI-1 scores used in this experiment would be considered low vocal fatigue, whereas only four teachers had scores greater than 24, representing clinically high vocal fatigue [23]. We will also further investigate the differences in validation and testing performances, as well as new features and techniques that can yield a better generalization and a deeper understanding of vocal fatigue clinically such as spectral analyses [29] and the analysis of sentence-level data.…”

Section: Discussionmentioning

confidence: 94%

“…Second, since some of the features used are amplitude-dependent, the lack of amplitude normalization in the sEMG signals extracted from different subjects could exacerbate the error under the inter-subject constraint. Third, we may still have insufficiently diverse data in order for the classifier to achieve better generalization (only four subjects scored high in the clinical range of the VFI-1, that is, >24 [23]). In the next sections, these three potential problems were investigated.…”

Section: Classifying Vowel Productions Based On Self-reported Vfi-1 S...mentioning

confidence: 99%

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Classification of Vocal Fatigue Using sEMG: Data Imbalance, Normalization, and the Role of Vocal Fatigue Index Scores

2021

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Our previous studies demonstrated that it is possible to perform the classification of both simulated pressed and actual vocally fatigued voice productions versus vocally healthy productions through the pattern recognition of sEMG signals obtained from subjects’ anterior neck. In these studies, the commonly accepted Vocal Fatigue Index factor 1 (VFI-1) was used for the ground-truth labeling of normal versus vocally fatigued voice productions. Through recent experiments, other factors with potential effects on classification were also studied, such as sEMG signal normalization, and data imbalance—i.e., the large difference between the number of vocally healthy subjects and of those with vocal fatigue. Therefore, in this paper, we present a much improved classification method derived from an extensive study of the effects of such extrinsic factors on the classification of vocal fatigue. The study was performed on a large number of sEMG signals from 88 vocally healthy and fatigued subjects including student teachers and teachers and it led to important conclusions on how to optimize a machine learning approach for the early detection of vocal fatigue.

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Section: Discussionmentioning

confidence: 94%

Section: Classifying Vowel Productions Based On Self-reported Vfi-1 S...mentioning

confidence: 99%

Classification of Vocal Fatigue Using sEMG: Data Imbalance, Normalization, and the Role of Vocal Fatigue Index Scores

2021

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“…Even in the absence of a perceivable voice disorder, a relevant health issue for these professionals is voice fatigue, ascribed to the perception of vocal effort and laryngeal discomfort, which are commonly mitigated by voice rest and [9] by means of individual behavioral strategies [10] . Although a shared definition of voice fatigue is currently missing, it should be considered a complex condition influenced by different underlying mechanisms across subjects and by individual characteristics [11] , [12] , [13] , [14] .…”

Section: Introductionmentioning

confidence: 99%

Vocal fatigue perceived in remote working by teachers of different school grades during COVID-19 pandemic

Cantarella

Negri²,

Bernardelli³

et al. 2023

Auris Nasus Larynx

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COVID-19 and Remote Learning: Predictive Factors of Perceived Improvement or Worsening of the Voice in Brazilian Teachers

et al. 2021

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Objectives . Given the transformations in teaching modalities as a result of the COVID-19 pandemic, this study aimed to evaluate the factors associated with vocal self-perception during the pandemic and to investigate the ability of selected variables to predict changes in vocal self-perception in teachers with professional vocal demands during the investigated period. Study design . Cross-sectional survey. Method . A total of 1,126 teachers of both genders (ages 19 to 78 years, average 43.23 years) who were participating in remote teaching activities using their voice answered the online questionnaire. To characterize vocal changes (improvement, worsening or no change), the teachers’ self-assessment of their voice quality was considered; the association between the variables reported in the questionnaire and vocal changes was analyzed, as was the capacity of these variables for predicting voice changes. Results . Most of the teachers worked at a public school, reported having no previous voice changes or difficulties with their voice during online classes and indicated that they began to use their voice less often and with reduced intensity during the pandemic. Vocal demands were divided between teaching synchronous online classes, recording video lessons and participating in online meetings. Several variables were associated with voice improvement and worsening, and some had the capacity to predict voice changes: improvement in voice quality was associated with working in a public school or as an autonomous teacher, reduced voice use and, to a lesser extent, participating in online meetings, certain vocal habits and a reduction in vocal symptoms; worsening voice quality was associated with working in a private school, increased voice use, using the voice at a higher intensity, difficulty with the voice in online classes, vocal habits and vocal symptoms. Conclusion . The context of remote education has changed the way teachers communicate during classes, and monitoring is necessary to ensure that necessary adaptations are made to preserve the voice and ensure effective communication.

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Vocal Fatigue Index in Teachers Using Mokken Analysis

Cited by 7 publications

References 21 publications

Classification of Vocal Fatigue Using sEMG: Data Imbalance, Normalization, and the Role of Vocal Fatigue Index Scores

Classification of Vocal Fatigue Using sEMG: Data Imbalance, Normalization, and the Role of Vocal Fatigue Index Scores

Vocal fatigue perceived in remote working by teachers of different school grades during COVID-19 pandemic

COVID-19 and Remote Learning: Predictive Factors of Perceived Improvement or Worsening of the Voice in Brazilian Teachers

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