Boothroyd Arthur
City University of New York, New York, New York, USA.
Ear Hear. 2008 Aug;29(4):479-91. doi: 10.1097/AUD.0b013e318174f067.
The purpose of this tutorial is to demonstrate the potential value of the Performance versus Intensity (PI) function in both research and clinical settings. The PI function describes recognition probability as a function of average speech amplitude. In effect, it shows the cumulative distribution of useful speech information across the amplitude domain, as speech rises from inaudibility to full audibility. The basic PI function can be modeled by a cubed exponential function with three free parameters representing: (a) threshold of initial audibility, (b) amplitude range from initial to full audibility, and (c) recognition probability at full audibility. Phoneme scoring of responses to consonant-vowel-consonant words makes it possible to obtain complete PI functions in a reasonably short time with acceptable test-retest reliability. Two examples of research applications are shown here: (a) the preclinical behavioral evaluation of compression amplification schemes, and (b) assessment of the distribution of reverberation effects in the amplitude domain. Three examples of clinical application show data from adults with different degrees and configurations of sensorineural hearing loss. In all three cases, the PI function provides potentially useful information over and above that which would be obtained from measurement of Speech Reception Threshold and Maximum word recognition in Phonectically Balanced lists. Clinical application can be simplified by appropriate software and by a routine to convert phoneme recognition scores into estimates of the more familiar whole-word recognition scores. By making assumptions about context effects, phoneme recognition scores can also be used to estimate word recognition in sentences. It is hard to escape the conclusion that the PI function is an easily available, potentially valuable, but largely neglected resource for both hearing research and clinical audiology.
本教程的目的是展示性能与强度（PI）函数在研究和临床环境中的潜在价值。PI函数将识别概率描述为平均语音幅度的函数。实际上，它显示了随着语音从听不见到完全可听，有用语音信息在幅度域上的累积分布。基本的PI函数可以用一个三次指数函数来建模，该函数有三个自由参数，分别表示：（a）初始可听阈值，（b）从初始可听到完全可听的幅度范围，以及（c）完全可听时的识别概率。对辅音-元音-辅音单词的反应进行音素评分，使得能够在相当短的时间内以可接受的重测信度获得完整的PI函数。这里展示了两个研究应用实例：（a）压缩放大方案的临床前行为评估，以及（b）幅度域中混响效应分布的评估。三个临床应用实例展示了来自不同程度和类型感音神经性听力损失成年人的数据。在所有这三种情况下，PI函数提供了超出从语音接受阈值测量和语音平衡列表中的最大单词识别所获得信息的潜在有用信息。通过适当的软件和将音素识别分数转换为更熟悉的全词识别分数估计值的程序，可以简化临床应用。通过对上下文效应做出假设，音素识别分数也可用于估计句子中的单词识别。很难不得出这样的结论：PI函数是听力研究和临床听力学中一种容易获得、潜在有价值但在很大程度上被忽视的资源。
