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.

中文翻译：

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DARF：数据减少的 FADE 版本，用于模拟真实助听器的语音识别阈值

开发和选择助听器是一个耗时的过程，可通过使用客观模型进行简化。此前，听觉辨别实验 (FADE) 框架准确模拟了助听器算法的优势，均方根预测误差低于 3 dB。一个 FADE 模拟需要几个小时的（未）处理的信号，这在信号必须被记录时是有障碍的。我们提出并评估了数据减少的 FADE 版本 (DARF)，该版本有助于模拟无法以数字方式处理但只能实时记录的信号。DARF 使用（德语）矩阵句测试的大约 30 分钟的记录和处理信号来模拟一个语音识别阈值 (SRT)。进行了基准实验以比较 DARF 和标准 FADE 对固定掩蔽器 (1 dB) 表现出小的差异，但与强烈波动的掩蔽器 (5 dB) 表现出更大的差异。听力障碍和助听器算法似乎减少了差异。在语音识别方面模拟了助听器的好处，使用三对真正的助听器在静音 ($\geq$8 dB)、在同一位置的固定和波动掩蔽器中 (stat. 2 dB;fluct. 6 dB) 和空间分离的语音和噪声信号（统计。$\geq$8 dB；波动。8 dB）。与文献数据相比，模拟是合理的，但与经验数据的比较仍然是开放的。DARF 有助于在真实环境中使用具有未知信号处理的真实设备进行客观的 SRT 模拟。然而，对未知信号处理设备的 DARF 验证仍悬而未决，因为它只用三个类似的设备进行了测试。尽管如此，DARF 可用于改进以及开发或基于模型的助听器验配。