# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 3 Voice Maps in Practice
### Finding a Representative Value  
Often a value that is intended to be characteristic of an individual is constructed by measuring on several sustained vowels and reporting the average. But such vowels are often elicited in a way that might not be representative, as discussed above. Instead, voice mapping of connected speech or some other relevant task can be completed. The \"density\" layer provided by most voice mapping systems is in effect a 2D histogram of SPL and \(f_{\text{o}}\). Using the bespoke voice-mapping system FonaDyn (Appendix A, and [46]) that analyzed only the voiced segments, [PERSON] and [PERSON] [18] recorded multiple readings of the _Rainbow Passage_. In the resulting 2D histograms they defined, as the most representative speech region, the 50% of phonations around the main peak of the distribution, and arbitrarily named it the \"T region\". A metric mean within this rather small and most visited region should be somewhat more representative than the mean of the entire reading. An example is shown in Figure 7.  
But why go to such lengths, just to get one representative value? For one thing, the mapping reveals whether or not the independent variables \(f_{\text{o}}\) and SPL have a _unimodal distribution_. We stated earlier that the distribution of \(f_{\text{o}}\) and SPL, as shown by the \"density\" layer in Figure 7a,b, has little effect on the _topography_ of the metric's scalar field. However, if we are asking which values of the metric can be considered typical or representative in connected speech, then this distribution _vill_ matter. Especially, if the distribution has several modes, then _any_ descriptive statistics will be called into question; which of the modes is the relevant one? This must be an informed, manual judgment. Assuming that the distribution is indeed unimodal, will using a distribution mean from a voice map make a difference, compared to the legacy sampling of a small number of sustained vowels? Well, it can, and a follow-up example shows how. In [18], recordings such as the one shown in Figure 7 were made of 26 adults (13 \(\circ\), 13 \(\circ\), group A) and 22 prepubertal children (group C). Figure 8 shows the descriptive statistics of the EGG contact quotient \(Q_{\text{ci}}\) over \(\Gamma\) for all those participants, based on the subset within their respective \(\Gamma\) only, of /a:/ productions sustained for the VRP. For each participant, these statistics are based on the contents of on average 22 cells in their own \(\Gamma\) region, which contained data from 1300-3400 phonatory cycles per person (c.f. the black grid in Figure 7c). From Figure 8 it is very clear that there is considerable inter-individual variation. This is typical for _any_ metric that we measure from the voice. Note that the standard deviations in \(Q_{\text{ci}}\) in Figure 8 were computed from the per-cell means in the \(\Gamma\) region only--not over the entire SRP. We see also that there seems to be a difference in \(Q_{\text{ci}}\) between the groups, but we would like to subject this to a robust statistical test. We now have estimates of the mean and standard deviation for each participant. Let us assume a normal distribution _within_ participants, even though this is overly simplistic; this assumption makes our example a best-case scenario, while reality is even worse. We can now draw one sample at random from each of the 48 participant distributions, _as if_ we had analyzed only one properly elicited sustained vowel from each participant. We can then test for group differences on the basis of the outcome of these random draws, using a two-sided \(t\)-test, and compute the \(p\)-value for that test. By repeating such a draw, say, a thousand times, we can estimate the  
Figure 7: Finding the “most habitual” speech: a vocally healthy adult male read the _Rainbow Passage_ 3 times, in a quiet environment. Panels (**a,b**) show the number of cycles phonated in each cell, on a log gray scale. This is the SRP, in the form of a 2D histogram.