# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 3 Voice Maps in Practice
### Finding a Representative Value
In (**b**), a 5-cycle minimum threshold per cell has been imposed, to suppress outliers. The black grid in (**b**) marks the \(\Gamma\) (gamma) region that delimits the central quartiles, that is, the most populated cells that contain 50% of the total cycle count. Panel (**c**) shows a voice map of the same person’s full voice range on the vowel /a:/ (note the change of scale), with \(\Gamma\) overlaid, and colour representing the metric \(Q_{\Delta}\) (normalized peak dEGG). Here, the full green colour represents absence or near-absence of vocal fold contacting. We note in passing how \(\Gamma\), the “core” of the habitual speech, lies just above the green area. Presentation adapted from [18] (Supplement 1). likelihood of detecting the real group difference based on the \(p\) value. Figure 9 shows the outcome of such a Monte Carlo simulation, using this data set. For this particular case, the 1000 averages from the random draws confirm our initial impression that there is a difference between the groups: in Figure 9, the ensembles of outcomes indicated by (A, blue) and (C, red) are visually distinct. However, the right panel in that figure shows that if we apply a criterion of \(p<0.05\), the probability of confirming a difference between these groups would be only 51.7%, if based on only one  
Figure 8: The descriptive statistics of the EGG contact quotient \(Q_{\text{ci}}\) over \(\Gamma\) for group A (adults) and group C (children), taken from sustained /a:/ productions within each participant’s \(\Gamma\) only. Filled circles are the means, vertical bars are the standard deviations. (Previously unpublished data, collected for [18].) Figure 9: Left: Monte Carlo simulation of one thousand possible outcomes of randomly sampling two groups, of which the individual participant distributions are known, assuming normality (from Figure 8). To the right, the same data, but sorted by increasing \(p\). On the left-hand vertical axis is the \(p\) value for the group difference observed for each draw; on the right-hand vertical axis are the sample averages (A) and (C) of groups A and C. The solid horizontal line indicates \(p=0.05\), while the dashed one indicates the \(p=0.002\) obtained when using the participant means instead of a random sample from each participant’s distribution. observation per participant. Using instead the per-participant means over their \(\Gamma\) region gave a corresponding \(p\) of 0.002 and a probability of confirming the difference of 93.8%. What we have done is in effect to increase the number of observations, not by sustaining more vowels, but by finding the metric's distribution within a relevant voice range for each participant and taking the mean of that distribution as the sole observation of that participant. This illustrates why obtaining a truly representative value per participant can be very important, especially if the intent is to test for group differences on the basis of \(p\) values. We recall here that such a test alone tells us nothing about the practical importance or clinical relevance of any observed difference. For a discussion of the interpretation of statistics, see the tutorial article by [PERSON] in this special issue [47].