Speech-in-noise tests use fixed signal-to-noise ratio (SNR) procedures to measure the percentage of correctly recognized speech items at a fixed SNR or use adaptive procedures to measure the SNR corresponding to 50% correct (i.e., the speech recognition threshold, SRT). A direct comparison of these measures is not possible yet. The aim of the present study was to demonstrate that these measures can be converted when the speech-in-noise test meets specific criteria. Formulae to convert between SRT and percentage-correct were derived from basic concepts that underlie standard speech recognition models. Information about the audiogram is not being used in the proposed method. The method was validated by comparing the direct conversion by these formulae with the conversion using the more elaborate Speech Intelligibility Index model and a representative set of 60 audiograms (r = 0.993 and r = 0.994, respectively). Finally, the method was experimentally validated with the Afrikaans sentence-in-noise test (r = 0.866). The proposed formulae can be used when the speech-in-noise test uses steady-state masking noise that matches the spectrum of the speech. Because pure tone thresholds are not required for these calculations, the method is widely applicable.

中文翻译：

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一种在语音噪声测试中在语音识别阈值和正确百分比分数之间转换的分析方法

Speech-in-noise 测试使用固定信噪比 (SNR) 程序来测量在固定 SNR 下正确识别的语音项目的百分比或使用自适应程序来测量对应于 50% 正确率的 SNR（即语音识别阈值，SRT）。目前尚无法对这些措施进行直接比较。本研究的目的是证明当噪声语音测试满足特定标准时，这些措施可以转换。在 SRT 和百分比正确率之间转换的公式源自标准语音识别模型基础的基本概念。在所提出的方法中没有使用关于听力图的信息。r  = 0.993 和r  = 0.994，分别）。最后，该方法通过南非荷兰语句子噪声测试 ( r  = 0.866)进行了实验验证。当语音噪声测试使用与语音频谱匹配的稳态掩蔽噪声时，可以使用建议的公式。由于这些计算不需要纯音阈值，因此该方法具有广泛的适用性。