论文标题
DARF：使用真实助听器的语音识别阈值模拟的数据减少淡出版本
DARF: A data-reduced FADE version for simulations of speech recognition thresholds with real hearing aids
论文作者
论文摘要
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。以前，听觉歧视实验的框架（淡出）精确模拟了助听器算法的均方根平方预测误差低于3 dB。一个褪色模拟需要几个小时的（联合国）处理的信号，当必须记录信号时，这是阻塞性的。我们提出和评估了数据还原的淡入淡出版本（DARF），该版本促进了无法以数字方式处理的信号的模拟，但只能实时记录。达夫（Darf）模拟了一个语音识别阈值（SRT），其中约30分钟记录了（德语）矩阵句子测试的信号。进行了基准实验，以比较DARF和标准褪色的固定掩蔽剂（1 dB）的差异很小，但较大的差异与强烈波动的掩膜器（5 dB）。听力障碍和助听器算法似乎减少了差异。助听器的益处在语音识别方面进行了模拟，并以三对真实的助听器（$ \ geq $ 8 dB），固定和波动的掩护者共同签名（Stat。2dB；Fluct。6dB），并在空间分开语音和噪音信号（Stat。$ \ geq $ 8 db; 8 dB）。与文献的数据相比，模拟是合理的，但是与经验数据的比较仍然开放。 Darf促进了带有实际设备的目标SRT模拟，并在真实环境中具有未知信号处理。然而，由于仅使用三个类似的设备测试了DARF对具有未知信号处理的设备的验证。尽管如此，DARF可用于改进以及开发或基于模型的助听器拟合。
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.
