## 4 Discussion  
The segmented glottal area can be affected by changing illumination, camera movement and larynx movement itself, which influences the calculated dynamic ranges (maximum minus minimum of the glottal area in 1 cycle). Hence the dynamic ranges may increase or decrease over time for some segments of the signal. This also explains the statistically significant change in _AVI_ between all groups of sequence lengths in contrast to the other unaffected APM. _AVI_ does not compare the dynamic ranges of consecutive cycles in pairs but instead compares each single dynamic range to an average dynamic range calculated for all cycles (see Table 1). For this reason, _AVI_ is more sensitive to long term changes in the signal. As the dynamic ranges continue to increase or decrease in the signal course, the distance between the average dynamic range and the dynamic ranges of each cycle increases with the signal length, which in turn affects the AVI. As opposed to this, the influence of such long-term effects on perturbation parameters comparing only neighboring cycles does not grow with the sequence length. Analogous to _AVI_ also _Period Variability Index_ (_PVI_) compares an average cycle length to every single cycle length. The reason why it does not change statistically significantly is that, with constant phonation, the cycle lengths do not increase or decrease over time and hence no long-term effects similar to the effects influencing the dynamic ranges occurred. \(RAP_{B}\) changes statistically significantly until at least a signal length of 20 consecutive cycles is reached. In contrast \(RAP_{K}\), which is a normalized version of \(RAP_{B}\), does not show statistically significant changes. In a previous work it was found that the maximum reachable value of \(RAP_{K}\) depends on the number of analyzed cycles [51], which is not the case for \(RAP_{B}\), if the sequence  
Figure 7: Energy Perturbation measures (EPM): (**a**) normalized group means, (**b**) standard deviation, (**c**) maximum value, (**d**) minimum value. length exceeds five cycles. Hence it seems natural to assume that \(RAP_{K}\) changes more strongly with changing sequence lengths than \(RAP_{B}\). Still \(RAP_{K}\) was the more stable measure in this study. For that reason, it can be assumed that for healthy female subjects \(RAP_{K}\) is more consistent for different sequence lengths than \(RAP_{B}\). Nevertheless because of the previous findings regarding the maximum reachable values, there is the possibility that for other types of phonation, for example voices with high period perturbation [3; 52], \(RAP_{B}\) would be more consistent than \(RAP_{K}\) for different sequence lengths of GAW-cycles. _APQ3_ only deviated statistically significantly between a sequence length of five analyzed cycles and the larger sequence lengths (with exception of the 5 cycles/100 cycles pairing). This could be the case since \(APQ3\) seems to be generally less stable than comparable parameters like _MShim_. In Figure 8a, a series of ten consecutive dynamic ranges is depicted for which the difference in behavior between \(APQ3\) and exemplary _MShim_ is clearly visible. For the different intervals of five cycles and the entire ten cycles, \(APQ3\) and _MShim_ were calculated. _MShim_ behaves consistently across the various intervals and the _MShim_ value for all ten consecutive cycles lies in between the values for the shorter intervals. In contrast, \(APQ3\) varies more strongly for the different five-cycle intervals and additionally the \(APQ3\) value calculated over all ten cycles is lower than the \(APQ3\) value for most of the shorter intervals. Figure 8b depicts the period lengths for the same subject. In contrast to the dynamic ranges, they are generally much more regular. Hence for this example, the \(PPQ3\) values that are calculated using the same formula as the \(APQ3\) values, but using period lengths instead of dynamic ranges, do not change at all for different starting positions.