Prevalence of Occupational Voice Disorder Among Teachers of Kathmandu District, Nepal

Objective: To compare voice related quality of life of smoker and non-smoker university teachers. Method: This Cross-Sectional descriptive study was conducted at Riphah International University over a period of six months January to June, 2022. A sample of N=352 University teachers of both genders, aged 25 to 65 years, who were faculty members and working at least 8 hours per day in teaching positions with at least one-year experience were included in the study. Demographic sheet, Voice Related Quality of Life (VRQOL) and Voice Handicap Index (VHI) were used for data collection and analysis conducted on SPSS Version 21. Mean scores of VRQOL and VHI for smokers and non-smokers were compared using Mann Whitney U Test. & Spearman’s correlation was utilized to determine any association between the tool scores. P<0.01 was considered significant. Results: Results reveal that the mean score of Voice related quality of life scale was significantly (p=0.000) higher in smokers compared to non–smokers indicating worse voice quality in smokers. Similarly, voice handicap index scores were much higher in smokers (p=0.000) indicating more handicap in the smokers. Conclusion: The study concludes that smoking has a detrimental effect on voice and voice related quality of life of university teachers and voice related quality of life as determined by VRQOL scale is significantly better in non-smokers.

Smoking has detrimental effects on voice related Quality of Life of University Teachers

In the medical field, identifying various pathological conditions poses a crucial challenge because it requires an invasive and contact-based data extraction technique. Therefore, non-invasive and non-contact forms of vital data, such as speech signals, can be used to identify various pathological conditions. Speech signals have distinguishing phonetic characteristics that change when a pathological condition occurs in the human body. By using these changes, various pathological signals can be classified by training machine learning and deep learning models with the acoustic features of speech signals. This work proposes the acoustic spectrogram transformer, where all the layers in the transformer are trained using acoustic characteristics extracted from the speech signals of voice and lung disease patients. Mel-frequency cepstral coefficients (MFCCs), Mel spectrograms, and spectral variables like centroid, bandwidth, roll-off, and zero-crossing rate are used for feature extraction from the voice and lung dataset. These acoustic features train the transformer blocks and depth-adaptive parameters, enabling the model to capture complex patterns for effective signal classification. Along with this architecture, the model consists of frequency-focused attention mechanisms used to extract spectral characteristics that are most indicative of pathological conditions. Meanwhile, multiple pooling strategies are employed for the effective aggregation of temporal information. Due to this targeted design, the system serves as an effective clinical tool for classification, minimizing computational complexity and achieving an accuracy of about 83% in voice pathology classification and 99% in lung pathology classification. 

Detection of Voice and Lung Pathological Signal Using Acoustic Spectrogram Transformers

Background: Voice disorders cause undesirable effects on school teachers such as reducing their quality of life, decrease in work performance affecting the quality of education, job absenteeism, and reduced social activities as well.
Methodology: Voice disorder among teachers was assessed through the “Voice handicap Index”, which was developed by Jacobson et al in 1997 to self-assess the severity of voice disorder in dysphonia patients
Results: The overall prevalence of voice disorder among government school teachers was found to be 46.5% with a 95% confidence interval from 41.5% to 51.5%. Using Backward Wald, the binary logistic regression analysis showed that female teachers (AOR 1.6, p<0.01*), teaching experience less than 10 years (AOR 2.4, p<0.01*), and teaching hours more than 21 hours per week (AOR 6.7, p<0.01*) had a significant association with voice disorders.
Conclusion: The prevalence of voice disorder was found to be high since teachers are professional voice users. The study also recommends that teachers must receive the required health education on the ergonomic risk factors that they will come into contact with in their daily lives as part of their jobs.

Epidemiology Of Voice Disorders Among Government School Teachers - An Analytical Cross-Sectional Study from Kancheepuram District

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Epidemiology Of Voice Disorders Among Government School Teachers - An Analytical Cross-Sectional Study from Kancheepuram District