  Developing and selecting hearing aids is a time consuming process which is
simplified by using objective models. Previously, the framework for auditory
discrimination experiments (FADE) accurately simulated benefits of hearing aid
algorithms with root mean squared prediction errors below 3 dB. One FADE
simulation requires several hours of (un)processed signals, which is
obstructive when the signals have to be recorded. We propose and evaluate a
data-reduced FADE version (DARF) which facilitates simulations with signals
that cannot be processed digitally, but that can only be recorded in real-time.
DARF simulates one speech recognition threshold (SRT) with about 30 minutes of
recorded and processed signals of the (German) matrix sentence test. Benchmark
experiments were carried out to compare DARF and standard FADE exhibiting small
differences for stationary maskers (1 dB), but larger differences with strongly
fluctuating maskers (5 dB). Hearing impairment and hearing aid algorithms
seemed to reduce the differences. Hearing aid benefits were simulated in terms
of speech recognition with three pairs of real hearing aids in silence ($\geq$8
dB), in stationary and fluctuating maskers in co-located (stat. 2 dB; fluct. 6
dB), 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 objective
SRT simulations with real devices with unknown signal processing in real
environments. Yet, a validation of DARF for devices with unknown signal
processing is still pending since it was only tested with three similar
devices. Nonetheless, DARF could be used for improving as well as for
developing or model-based fitting of hearing aids.
