# A Class of Perfectly Secret Autonomous Low-Bit-Rate Voice Communication Systems
## 3 Identification and Analysis of Possible Sources of Common Randomness  
The previous analysis shows that in the open speech phase, two sources of common randomness, \(CR\big{(}S,\ \hat{S}\big{)}\) and \(CR\big{(}\hat{S}_{i},\ \hat{S}\big{)}\), are available. In order to evaluate which of these sources is more suitable, we conducted an experimental evaluation in real conditions of communication with the vPCP-V system (see Figure 5) and Vlatacom True Random Number Generator (vTRNG) [19,33]; see Figure 6.  
For experimental evaluation, we formed a test set consisting of 24 speech signals with speakers reading the provided text. In 14 cases, the text was unique, while in the remaining 10 cases, the speakers recorded the repeated text. The signals have durations between 32 s and 59 s, are sampled at a frequency of 8 kHz, and are discretized with 16 bits per sample.  
Figure 7 shows the cross-correlation function between the original input speech signal S and the synthesized received signal \(\hat{S}\) for sample No. 1 from the test set. Figure 8 shows  
Figure 4: Generic scheme of the proposed APS-VCS. The pseudo-random string \(C\) is the one-time secret key of the Vernam cipher, which is read from the Secret Key FIFO buffer synchronously on [PERSON]’s and [PERSON]’s sides. The contents of the FIFO buffer are filled with distilled secret keys received by applying the SKD protocol to the common randomness source \(CR\big{(}\hat{S}_{IA},\hat{S}_{IB}\big{)}\). Signals \(\hat{S}_{IA}\) and \(\hat{S}_{IB}\) were obtained by local LP syntheses over the same LSP parameters synchronously read from the FIFO buffer.  
Figure 5: Vlatacom Personal Crypto Platform for voice encryption designed for use in any available communication system (Voice over Internet Protocol (VoIP), public, landline, mobile, or satellite).  
the corresponding cross-correlation function between the locally synthesized speech signal \(\hat{S}_{l}\) and the synthesized received signal \(\hat{S}\) for the same speech sample. From the examples shown, it is clear that the correlation of the common randomness source \(CR\big{(}\hat{S}_{l},~{}\hat{S}\big{)}\) is, by an order of magnitude, higher than the \(CR\big{(}\hat{S},~{}\hat{S}\big{)}\) source.  
Figure 8: Cross-correlation between the locally synthesized speech signal \(\hat{S}_{l}\) on the transmitter side and the synthesized signal on the receiver side \(\hat{S}\) for speaker No. 1 from the test set.  
Figure 6: Vlatacom True Random Number Generator based on a natural process entropy source with built-in randomness checking system.  
Figure 7: Cross-correlation between the original input speech signal \(S\) and the synthesized received signal \(\hat{S}\) for speaker No. 1 from the test set.  
This fact was confirmed across the entire test sample. Figure 9 shows the logarithm ratio \(\frac{CrossCorr(\hat{S}_{l},\,\hat{S})}{CrossCorr(S,\,\hat{S})}\) for all 24 test samples of speech signals. Only in the case of test sample No. 3 is this ratio less than 1. This indicates that the cross-correlation \(CrossCorr(\hat{S}_{l},\,\hat{S})\) is almost always significantly higher than the cross-correlation \(CrossCorr(S,\,\hat{S})\). Therefore, we have decided to use the source \(CR(\hat{S}_{l},\,\hat{S})\) for the execution of the SKD protocol in the open phase of communication of the APS-VCS system.