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Developing and selecting hearing aids is a time consuming process which issimplified by using objective models. Previously, the framework for auditorydiscrimination experiments (FADE) accurately simulated benefits of hearing aidalgorithms with root mean squared prediction errors below 3 dB. One FADEsimulation requires several hours of (un)processed signals, which isobstructive when the signals have to be recorded. We propose and evaluate adata-reduced FADE version (DARF) which facilitates simulations with signalsthat cannot be processed digitally, but that can only be recorded in real-time.DARF simulates one speech recognition threshold (SRT) with about 30 minutes ofrecorded and processed signals of the (German) matrix sentence test. Benchmarkexperiments were carried out to compare DARF and standard FADE exhibiting smalldifferences for stationary maskers (1 dB), but larger differences with stronglyfluctuating maskers (5 dB). Hearing impairment and hearing aid algorithmsseemed to reduce the differences. Hearing aid benefits were simulated in termsof speech recognition with three pairs of real hearing aids in silence ($\geq$8dB), in stationary and fluctuating maskers in co-located (stat. 2 dB; fluct. 6dB), and spatially separated speech and noise signals (stat. $\geq$8 dB; fluct.8 dB). The simulations were plausible in comparison to data from literature,but a comparison with empirical data is still open. DARF facilitates objectiveSRT simulations with real devices with unknown signal processing in realenvironments. Yet, a validation of DARF for devices with unknown signalprocessing is still pending since it was only tested with three similardevices. Nonetheless, DARF could be used for improving as well as fordeveloping or model-based fitting of hearing aids.

DARF: A data-reduced FADE version for simulations of speech recognition thresholds with real hearing aids