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From Voice Signals to Voice Maps
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Abstract
This article is intended as an introductory tutorial for technically inclined clinicians, vocologists and voice pedagogues who want to understand the principles and potentials of voice mapping. Voice mapping has its origins in the Voice Range Profile, or phonetogram, but it is less concerned with the extremes of the voice range, and more with what happens
within
a relevant range of the voice. It is a voice instrumentation paradigm that is intended to improve the evidential value of voice measurements. It exposes and automatically accounts for the strong co-variation that most voice metrics exhibit with fundamental frequency and sound level. Very many data points are automatically collected in a short time, and their means are mapped by colour onto maps. This results in a robust representation of voice status and function. While individual voices are very different, a voice map’s appearance is reproducible within individuals. Comparing maps across interventions gives rich information, even on subtle changes in a voice. Moreover, by statistically clustering multiple metrics, phonation types can be identified and mapped automatically, thereby enhancing clinical relevance, and facilitating a deeper understanding of voice data.
Title: From Voice Signals to Voice Maps
Description:
Abstract
This article is intended as an introductory tutorial for technically inclined clinicians, vocologists and voice pedagogues who want to understand the principles and potentials of voice mapping.
Voice mapping has its origins in the Voice Range Profile, or phonetogram, but it is less concerned with the extremes of the voice range, and more with what happens
within
a relevant range of the voice.
It is a voice instrumentation paradigm that is intended to improve the evidential value of voice measurements.
It exposes and automatically accounts for the strong co-variation that most voice metrics exhibit with fundamental frequency and sound level.
Very many data points are automatically collected in a short time, and their means are mapped by colour onto maps.
This results in a robust representation of voice status and function.
While individual voices are very different, a voice map’s appearance is reproducible within individuals.
Comparing maps across interventions gives rich information, even on subtle changes in a voice.
Moreover, by statistically clustering multiple metrics, phonation types can be identified and mapped automatically, thereby enhancing clinical relevance, and facilitating a deeper understanding of voice data.
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