The Articulation Index (AI) and Speech Intelligibility Index (SII) predict intelligibility scores from measurements of speech and hearing parameters. One component in the prediction is the “importance function,” a weighting function that characterizes contributions of particular spectral regions of speech to speech intelligibility. Previous work with SII predictions for hearing-impaired subjects suggests that prediction accuracy might improve if importance functions for individual subjects were available. Unfortunately, previous importance function measurements have required extensive intelligibility testing with groups of subjects, using speech processed by various fixed-bandwidth low-pass and high-pass filters. A more efficient approach appropriate to individual subjects is desired. The purpose of this study was to evaluate the feasibility of measuring importance functions for individual subjects with adaptive-bandwidth filters. In two experiments, ten subjects with normal-hearing listened to vowel-consonant-vowel (VCV) nonsense words processed by low-pass and high-pass filters whose bandwidths were varied adaptively to produce specified performance levels in accordance with the transformed up-down rules of Levitt [(1971). J. Acoust. Soc. Am. >49, 467–477]. Local linear psychometric functions were fit to resulting data and used to generate an importance function for VCV words. Results indicate that the adaptive method is reliable and efficient, and produces importance function data consistent with that of the corresponding AI/SII importance function.
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机译:清晰度指数(AI)和语音清晰度指数(SII)通过语音和听力参数的测量来预测清晰度分数。预测中的一个组成部分是“重要性函数”,它是表征特定语音区域对语音清晰度的贡献的加权函数。先前对听力障碍受试者进行SII预测的工作表明,如果可以获得针对各个受试者的重要性函数,则预测准确性可能会提高。不幸的是,以前的重要性函数测量需要使用由各种固定带宽的低通和高通滤波器处理过的语音,对一组对象进行广泛的清晰度测试。希望有一种适用于个别受试者的更有效的方法。这项研究的目的是评估使用自适应带宽滤波器测量单个对象的重要性函数的可行性。在两个实验中,十名听力正常的受试者听了由低通和高通滤波器处理的元音-辅音-元音废话,其带宽根据转换后的上下自适应地变化以产生指定的性能水平Levitt的规则[(1971)。 J. Acoust。 Soc。上午。 > 49 strong>,467-477]。局部线性心理测验函数适合所得数据,并用于生成VCV单词的重要性函数。结果表明,该自适应方法是可靠且有效的,并且产生了与相应的AI / SII重要性函数一致的重要性函数数据。
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