In Figure 5.14, we see results of MCMC-UMS, similar to Figure 5.11. Rather than using synthetic data, we captured a segment of 768 samples of human speech at 12kHz, then calculated the MFCC features . With this feature value fixed, we used MCMC-UMS to produce random spectra. On the top graph, we see one random MCMC-UMS sample. On the bottom, we show the LP solution, the MaxEnt solution , and the average of 10000 full MCMC-UMS iterations. Once again we can conclude that precisely predicts the mean of the MCMC-UMS generated spectra. Important to note is that is a very smooth spectral estimate that is, at least visually, very satisfactory. Our proposed method may be preferred to open-ended MFCC synthesis methods  since the resulting spectrum is feature-reproducing and has optimal smoothness as a result of satisfying the maximum entropy rule. This is because maximizing the entropy (5.14) is the same as maximizing the spectral flatness [36,25].