# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
###### Abstract  
Individual acoustic and other physical metrics of vocal status have long struggled to prove their worth as clinical evidence. While combinations of metrics or \"features\" are now being intensely explored using data analytics methods, there is a risk that explainability and insight will suffer. The voice mapping paradigm discards the temporal dimension of vocal productions and uses fundamental frequency (\(f_{\text{o}}\)) and sound pressure level (SPL) as independent control variables to implement a dense grid of measurement points over a relevant voice range. Such mapping visualizes how most physical voice metrics are greatly affected by \(f_{\text{o}}\) and SPL, and more so individually than has been generally recognized. It is demonstrated that \(iff_{\text{o}}\) and SPL are not controlled for during task elicitation, repeated measurements will generate \"elicitation noise\", which can easily be large enough to obscure the effect of an intervention. It is observed that, although a given metric's dependencies on \(f_{\text{o}}\) and SPL often are complex and/or non-linear, they tend to be systematic and reproducible in any given individual. Once such personal trends are accounted for, ordinary voice metrics _can_ be used to assess vocal status. The momentary value of any given metric needs to be interpreted in the context of the individual's voice range, and voice mapping makes this possible. Examples are given of how voice mapping can be used to quantify voice variability, to eliminate elicitation noise, to improve the reproducibility and representativeness of already established metrics of the voice, and to assess reliably even subtle effects of interventions. Understanding variability at this level of detail will shed more light on the interdependent mechanisms of voice production, and facilitate progress toward more reliable objective assessments of voices across therapy or training.  
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The voice has a wide range of different behaviours, for instance, from soft to loud and from low notes to high notes. These behaviours originate in a biomechanical system with fluctuating properties whose function depends on such diverse circumstances as geometries, material properties of tissues, muscular forces, aerodynamics, and acoustics, to name only the primary ones. Furthermore, as individuals we have vocal folds and vocal tracts that are just as unique to the individual as our faces. These unique differences leave imprints on the sound and also on other signals that we can get from a voice, and this large variability makes the voice a rich and wondrous channel of communication.  
As any voice clinician or researcher knows, this complexity also poses great challenges to the objective, quantitative assessment of vocal status and function. So far, subjective assessment has remained very important, but to be reliable it requires very careful staff training and considerable clinical experience. A great deal of work has therefore been invested in the search for objective metrics that could be reliable indicators of a person's vocal status. Such work has shown that, when measuring physical entities from vocal signals and images, the clinical evidence obtained from individual metrics tends to be weak [1; 2; 3].