Prevalence of Dysphonia in Metal Singers and the Impact of Vocal Education: A Subjective Analysis

Population characteristics and self-assessment of speaking and singing voice in Polish contemporary commercial music singers-an exploratory, cross-sectional study

Background

The domination of the Contemporary Commercial Music (CCM) industry in music markets has led to a significant increase in the number of CCM performers. Performing in a wide variety of singing styles involves exposing CCM singers to specific risk factors potentially leading to voice problems. This, in turn, necessitates the consideration of this particular group of voice users in the Occupational Health framework. The aim of the present research was threefold. First, it sought to profile the group of Polish CCM singers. Second, it was designed to explore the prevalence of self-reported voice problems and voice quality in this population, in both speech and singing. Third, it aimed to explore the relationships between voice problems and lifetime singing involvement, occupational voice use, smoking, alcohol consumption, vocal training, and microphone use, as potential voice risk factors.

Materials and methods

The study was conducted in Poland from January 2020 to April 2023. An online survey included socio-demographic information, singing involvement characteristics, and singers' voice self-assessment. The prevalence of voice problems was assessed by the Polish versions of the Vocal Tract Discomfort Scale (VTDS) and the Singing Voice Handicap Index (SVHI). Also, a self-reported dysphonia symptoms protocol was applied. The perceived overall voice quality was assessed by a Visual Analogue Scale (VAS) of 100 mm.

Results

412 singers, 310 women and 102 men, completed the survey. Nearly half of the studied population declared lifetime singing experience over 10 years with an average daily singing time of 1 or 2 h. 283 participants received vocal training. For 11.4% of respondents, singing was the primary income source, and 42% defined their career goals as voice-related. The median scores of the VTDS were 11.00 (0-44) and 12.00 (0-40) for the Frequency and Severity subscales, respectively. The median SVHI score of 33 (0-139) was significantly higher than the normative values determined in a systematic review and meta-analysis (2018). Strong positive correlations were observed between SVHI and both VTD subscales: Frequency (r = 0.632, p < 0.001) and Severity (r = 0.611, p < 0.001). The relationships between most of the other variables studied were weak or negligible.

Conclusion

The examined CCM singers exhibited substantial diversity with regard to musical genre preferences, aspirations pertaining to singing endeavors, career affiliations, and source of income. Singing voice assessment revealed a greater degree of voice problems in the examined cohort than so far reported in the literature, based on the SVH and VTDS.

The Vocal Hygiene Treatment Programs for Singers: A Systematic Review

OBJECTIVES

This systematic review aims to explore the effectiveness of voice health education interventions among singers, particularly focusing on vocal hygiene treatment programs tailored for professional voice users.

STUDY DESIGN

Systematic review.

METHODS

Preferred Reporting Items on Systematic Reviews and Meta-Analysis guidelines were followed to conduct this systematic review. Comprehensive searches were conducted in PubMed, Web of Science, Scopus, Science Direct, and Cochrane Library databases. Four articles were selected for detailed review. The studies were evaluated using the Effective Public Health Practice Project tool for quality assessment.

RESULTS

The four reviewed studies primarily utilized the pretest-posttest design to examine the effectiveness of vocal hygiene interventions on singers' vocal health. Two studies investigated the effect of hydration as a treatment method, while the remaining two focused on vocal hygiene instruction. Significant improvements were observed in various vocal health parameters, including maximum phonation time, intensity, Dysphonia Severity Index, and number of daily vocal breaks taken.

CONCLUSION

This systematic review provides valuable insights into the efficacy of vocal hygiene treatment programs for singers. The positive outcomes observed in the reviewed studies underscore the importance of voice health education tailored to singers' specific needs. However, the limited number of eligible studies and the common limitation of small sample sizes highlight the need for further research in this area. Vocal health practitioners, educators, and researchers can utilize the findings of this review to develop evidence-based vocal hygiene interventions that promote the well-being and longevity of singers' vocal performance careers.

Towards Automated Vocal Mode Classification in Healthy Singing Voice-An XGBoost Decision Tree-Based Machine Learning Classifier

Auditory-perceptual assessment is widely used in clinical and pedagogical practice for speech and singing voice, yet several studies have shown poor intra- and inter-rater reliability in both clinical and singing voice contexts. Recent advances in artificial intelligence and machine learning offer models for automated classification and have demonstrated discriminatory power in both pathological and healthy voice. This study develops and tests an XGBoost decision tree based machine learning classifier to develop automated vocal mode classification in healthy singing voice. Classification models trained on mel-frequency cepstrum coefficients, MFCC-Zero-Time Windowing, glottal features, voice quality features, and α-ratios demonstrated 92% average F1-score accuracy in distinguishing metallic and non-metallic singing for male singers and 87% average F1-score for female singers. The model distinguished vocal modes with 70% and 69% average F1-score for male and female samples, respectively. Model performance was compared to human auditory-perceptual assessments of 64 corresponding samples performed by 41 professional singers. The model performed with approximating or subpar performance to human assessors on task-matched problems. The XGBoost gains observed across tested features reveal that the most important attributes for the tested classification problems were MFCCs and α-ratios between high and low frequency energy, with models trained on only these features achieving performance not statistically significantly different from the best tested models. The best automated models in this study do not yet match human auditory-perceptual discrimination but improve on previously reported F1-average accuracies in automated classification in singing voice.