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_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 fo and SPL, and more so individually than has been generally recognized. It is demonstrated that if f_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_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.