In an attempt to predict the outcomes of matrix sentence tests in different languages and various noise conditions for native listeners, the simulation framework for auditory discrimination experiments (FADE) and the extended Speech Intelligibility Index (eSII) is employed. FADE uses an automatic speech recognition system to simulate recognition experiments and reports the highest achievable performance as the outcome, which showed good predictions for the German matrix test in noise. The eSII is based on the short-time analysis of weighted signalto-noise ratios in different frequency bands. In contrast to many other approaches, including the eSII, FADE uses no empirical reference. In this work, the FADE approach is evaluated for predictions of the German, Polish, Russian, and Spanish matrix test in stationary and fluctuating noise conditions. The FADEbased predictions yield a high correlation (Pearsons R 2 = 0.94) with the empirical data and a root-mean-square (RMS) prediction error of 1.9 dB outperforming the eSII-based predictions (R 2 = 0.78, RMS = 4.2 dB). FADE can also predict the data of subgroups with only stationary or only fluctuating noises, while the eSII cannot. The FADE-based predictions seem to generalize over different languages and noise conditions.