<b><i>Objectives:</i></b> The objective of the present study was to explore the voice concerns and vocal and nonvocal habits of Vedic chanters. <b><i>Participants and Method:</i></b> A cross-sectional design was used to study 200 Vedic chanters using a 36-item questionnaire. This questionnaire was developed and administered to explore their voice concerns and vocal and nonvocal habits. Descriptive statistics were used to summarize the findings and K-means cluster analysis was carried out to identify clusters concerning voice quality and influence of habits. <b><i>Results:</i></b> The majority of Vedic chanters were involved in certain vocal habits such as use of loud voice while chanting and frequent throat clearing. Further, approximately half of the chanters expressed a concern towards their voice, while a vast majority experienced vocal fatigue. Cluster analysis helped in identification of 4 clusters: Vedic chanters with (1) good lifestyle and good voice characteristics (<i>n</i> = 107), (2) good lifestyle but bad voice characteristics (<i>n</i> = 15), (2) poor lifestyle and good voice characteristics (<i>n</i> = 51), and (4) poor lifestyle leading to bad voice characteristics (<i>n</i> = 27). <b><i>Conclusion:</i></b> The findings of the present study provide useful information on voice concern and vocal and nonvocal habits among a unique population of individuals. The current study of Vedic chanters highlights the need to have a detailed understanding of their specific voice usage, demands, and voice characteristics.
While acoustic speech analysis is non-invasive, the utility has been mixed due to the range of voice types. For vocal health practitioners to efficiently and quickly assess and document voice changes, knowing which voice parameter would be sensitive to vocal change is crucial. Using a database of 296 individual voices including 8 voice pathology types and typical voice samples, the sensitivity of a range of acoustic speech parameters to differentiate common voice pathology types was investigated. Both traditional and contemporary acoustic speech metrics were estimated for the samples using a custom MATLAB script and PRAAT (e.g., jitter, shimmer HNR, CPPS, Alpha ratio, PPE). Analysis then evaluate the predictability value of the metrics to discriminate pathology type. From the pool of parameters, 11 were able to identify pathological voices from normal controls and several of the parameters were more sensitive to some pathology. For example, CPPs and jitter values could discriminate neuropathological voices whereas HNR and Shimmer cold discriminate muscle-based pathologies. These results indicate how the sensitivity of acoustic speech metrics to the voice pathology types can allow for the identification of individual metrics (or combinations of metrics) which could be used to track changes in vocal health.
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