# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 4 Discussion and Conclusions  
### Choosing the Metrics for Voice Maps  
Intentionally, we have largely avoided the topic of the _interpretation_ of the metric scalar fields, asserting simply that \"voices are more different than you think\". The central topic of the present article has been understanding and dealing with _variability in_ the voice. This is a necessary but hitherto rather neglected prerequisite to measuring and understanding the voice itself. Following that, one will need to focus on selecting those metrics that are of the greatest relevance to the domain of study and to the chosen interpreting models. The domain could be clinical, pedagogical or research-oriented. For instance, when assessing vocal pathologies, the literature shows that metrics of irregularity of phonation will be needed, while for assessing elite singing, metrics of the resonance properties of the voice may be more relevant. For addressing specific research questions about voice production or voice perception, again the selection of metrics will be guided by the choice of models. For instance, for production studies, classical metrics such as contact quotient, MFDR, \(P_{\text{sub}}\), etc., are perfectly useable, while for perceptual studies one could map metrics such as loudness [52] or other psychoacoustic entities. The common principle, however, is to find a small set of relevant metrics with a low degree of mutual _residual_ information, once the universally strong covariations that are linked to SPL and \(f_{\text{o}}\) have been accounted for. This search is greatly facilitated by voice mapping, which does precisely this accounting, for each individual.  
Additionally, if metrics are chosen such that they can act also as _synthesis_ parameters [13] (Chapter 2.4), then their role and relative contributions can be evaluated by listening to the output of a synthesis model. If the re-synthesized voice sounds correct in all relevant respects, then this is a validation that the employed set of metrics is complete, at least perceptually so. This may seem to be a trivial assertion, but the voice map format facilitates the auditory exploration of how the perceptual roles of different parameters _also_ change across the voice range.  
One may then ask if any metric exists that is directly comparable across voices; one that would represent a basic physiologic aspect of phonation, such that all voices show a similar variation over \(f_{\text{o}}\) and SPL. In our experience, the best candidate for such a \"reference\" metric is the power of the fundamental partial, relative to the summed power of the remaining partials. Several variants occur in the literature, such as HRF for Harmonic Richness Factor [53], when referring to the spectrum of the glottal flow, or \(L_{\text{Rest/H1}}\)[31], referring to the radiated spectrum. \(L_{\text{Rest/H1}}\) is distributed in a similar way for all voices phonating in the same mechanism (M1 or M2). The audiophile would say that it represents the amount of harmonic distortion in a sound system. This metric represents a fundamental aspect of the voice physiology and the acoustic voice production mechanism, namely how much the voice source deviates from being a linear, time-invariant system, in the formal sense of the term. There is however no link of absolute metric values to \(f_{\text{o}}\) and SPL that will apply to all individuals.