DARF：数据简化的FADE版本，用于使用真实助听器模拟语音识别阈值
DARF: A data-reduced FADE version for simulations of speech recognition thresholds with real hearing aids
摘要：
开发和选择助听器是一个耗时的过程，使用客观模型可以简化这个过程。以前，听觉辨别实验（FADE）的框架准确地模拟了助听器算法的好处，均方根预测误差低于3 dB。一个FADE模拟需要几个小时的（未）处理信号，这在必须记录信号时是有障碍的。我们提出并评估了一个数据减少的FADE版本（DARF），这有利于模拟信号，不能进行数字处理，但只能实时记录。DARF用大约30分钟的（德语）矩阵句子测试的记录和处理信号模拟一个语音识别阈值（SRT）。进行基准实验以比较DARF和标准FADE，其对于静止掩蔽物（1dB）表现出小的差异，但是对于强烈波动掩蔽物（5dB）表现出较大的差异。听力障碍和助听器算法似乎减少了差异。在语音识别方面，使用三对真正的助听器在安静（$\geq$ 8 dB）、静止和波动的掩蔽物中进行了模拟。2 dB; fluct. 6 dB），以及空间分离的语音和噪声信号（统计。$\geq$ 8 dB;波动8 dB）。与文献中的数据相比，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SRT仿真。然而，对于具有未知信号处理的设备的DARF的验证仍然悬而未决，因为它仅用三个类似设备进行了测试。尽管如此，DARF可用于改善以及开发或基于模型的助听器验配。
英文摘要：
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.
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