Date of Completion
11-30-2018
Embargo Period
11-30-2018
Keywords
Head Voice, Bass Voice, Vocal Pedagogy
Major Advisor
Constance Rock
Associate Advisor
Alain Frogley
Associate Advisor
Peter Kaminsky
Field of Study
Music
Degree
Doctor of Musical Arts
Open Access
Open Access
Abstract
Over the last two centuries, knowledge of the voice has grown exponentially due to advances in science, technology, and medicine. One no longer needs to guess regarding certain functions of the instrument through pure empiricism, but rather, principles or ideas can be brought to the laboratory and tested through simulation and studies. In the last few decades, scientists and pedagogues have realized that registration is not solely the result of laryngeal musculature, but also has implications in the acoustic environment of the vocal tract. This discovery has completely changed the way a teacher may look at training a voice, or fixing vocal issues.
One avenue of voice training and science that has not received the same level of interest is the process in which we learn to sing. Significant strides have been made recently in the science of perceptual motor learning and is utilized to great effect in the field of Speech and Language Pathology. Given that the musical use of the voice is a highly complex motor skill, it is easy to appreciate the possible implications of borrowing theories and principles from motor learning science in order to better train singers. This study offers a discussion regarding commonly used terms in male registration, as well as a brief look at the history of male high voice singing. In addition, it explores certain principles of motor learning and subsequently how they can be employed to train the upper range of the low male voice. Detailed examples of exercises are provided, as well as short repertoire extracts for context.
Recommended Citation
Leathem, Anthony, "The Utilization of Perceptual Motor-Learning Principles for the Acquisition of Head Voice in the Post-Adolescent Bass Voice" (2018). Doctoral Dissertations. 2016.
https://digitalcommons.lib.uconn.edu/dissertations/2016