# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 2 Aspects of Variability in Voice
### Variations: Noise or Data?  
The abundant dependencies on \(f_{\circ}\) and SPL are only two aspects of the many sources of variation in metrics of the voice. Other aspects include the physics (turbulence, hysteresis, chaotic systems), the biomechanics (glottal posturing, lung volume, heart rate, etc.) [33], the neuromotor control (reaction times, intrinsic feedback loops, etc.), the phonetics (dependency on phonemes, speech rate, etc.), and the representation of the measured signal (artefacts of quantization and sampling). With all this variability, voice metrics appear to be inherently more or less noisy. However, all of the variation is not stochastic noise; much of it is simply due to factors that have not been controlled for. Often, the distribution of a metric can be informative as to which type of variation we are dealing with. Briefly, distributions that do not appear to conform to the central limit theorem, by approaching a normal distribution, are not stochastic; they are instead likely to indicate that there is a (re-)distributing mechanism that is producing the observed variation. Such analysis of the distribution of a metric along its own scale is unfortunately beyond the scope of this article, even though the data acquisition framework offered by the voice map paradigm will support it.  
To obtain good information on the shape of the scalar field of a metric, we need to sample the metric as densely as necessary over the voice field, at small intervals in \(f_{\circ}\) and SPL. In fact, the standard resolution of one semitone and one decibel is purely pragmatic; whether or not it is sufficient has not been shown. In the following sections, however, we will show that it is a great improvement on sparse sampling at a few effort levels. Importantly, we do _not_ need to make many repeated measurements in each point (cell), so long as the metric in question is well defined for each observation, nor do the data points have to be collected in any particular temporal sequence. The shape of the field will not depend on the order of acquisition, so long as the observed voice does not change appreciably over the course of the recording.  
Most scalar voice metrics are defined in relation to the phonatory cycle, which constitutes an elemental time step for new information [34]. Hence \(f_{\circ}\) is usually the maximum data update rate, provided that observations are made synchronously with the phonatory cycles. Only rarely will it be meaningful to make more than one observation per cycle. This implies a data rate of one or two hundred observations per second for adult speech, and up to over a thousand observations per second in female singing. When analysing period-to-period variations, which are known to be of clinical interest, the more contiguous cycles we can record, the better, since such metrics require a comparison of multiple cycles.  
In this context, the rate of observations should not be confused with the sampling rate of the signal. When we sample an acoustic or other signal at the consumer audio standard rate of 44,100 times per second, it is not the values of all those samples that interest us, but rather the shapes of the waveforms that the samples describe. Similarly, in high-speed video endoscopy, with image rates of 2-20 kHz, it is usually not the individual images that provide the interesting information, but rather the temporal behaviour in those image sequences of features that typically are evaluated for each phonatory cycle. The more data we can collect, the better such features can be resolved. Once the features of the voice map have emerged clearly enough, it is redundant to have the informant prolong the task. A key issue in the design of voice map protocols is therefore how to acquire an _adequate_ density of phonatory cycles across the relevant voice range. We will return to this issue, further on.