# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
### People Are Different, but Consistent  
Not only do practically all voice metrics depend a great deal on SPL and on \(f_{0}\)[16, 17], but these dependencies can also be quite different from person to person. Fortunately, the variation in a metric over the voice field (not over time) tends to be systematic and reproducible, so long as we are making repeated observations of the same individual. As an example, Figure 2 shows three repeated takes of running speech by four adult normophonic (vocally healthy) males, recorded for [PERSON] and [PERSON] [18]  
Figure 1: A healthy male amateur choir singer sang repeated _messa di vace_ exercises (soft-loud-soft on constant pitch) over a limited range of one octave, and four voice metrics were mapped. (**a**) EGG contact quotient \(Q_{\text{cl}}\)[15], (**b**) normalized peak EGG derivative \(Q_{\Delta}\), (**c**) EGG cycle-rate sample entropy, and (**d**) audio spectrum balance. The resulting images can be thought of as four different “layers” of one composite voice map. This voice map shows averaged data from over 60,000 phonatory cycles and took about six minutes to record. For these graphs, a minimum of five phonatory cycles was required in each cell. For semitones, the MIDI scale is used, in which middle C4 (261 Hz) is assigned the scale number 60. (Previously unpublished data, collected for [14].)  
From Figure 2 it will be clear that, while the map can be very different from that of another similar person performing the same task in the same conditions, people can consistently reproduce their own voice map for a given task. Another example, from eight trained female singers singing a nursery rhyme many times, can be found in [19] (Supplement B), which shows how within participants, EGG waveforms change very consistently on replications over the range of the song, but different singers exhibit very different regions for their typical waveforms. This means that the maps are showing relationships of metrics to SPL and \(f_{\text{o}}\) that are specific to a particular voice, i.e., that are properties of the voice production mechanism, and that would not readily emerge from point-wise sampling of single vowels. It means also that, in general, the effects of interventions can be assessed directly only _within_ persons. Still, that possibility is of great practical significance. We will show how the effects of interventions can be visualized by making maps pre- and post-intervention, and then constructing a new map of the differences.  
Figure 2: Voice maps of a given task tend to be similar within persons, but can be very different from one individual to another. As an example, these maps illustrate the variation with SPL,\(f_{\text{o}}\), and individual in the EGG quotient of contact by integration \(Q_{\text{di}}\)[15], over the habitual speech range. Four vocally healthy adult males (columns) performed three consecutive readings (rows) of the full _Rainbow Passage_. Each map results from about two minutes of reading, in a quiet recording booth. The \(Q_{\text{ci}}\) was measured for every phonatory cycle above a regularity threshold and averaged within each cell of 1 dB and 1 semitone on the map, in real time. The per-cell average \(Q_{\text{ci}}\) is colour-coded as in the horizontal bars (Previously unpublished data, collected for [18], where the acquisition is described in detail).