Department of Communication Disorders, University of Massachusetts, Amherst, Massachusetts 01003, USA.
J Acoust Soc Am. 2011 Dec;130(6):4032-43. doi: 10.1121/1.3641453.
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.
关节指数 (AI) 和语音可懂度指数 (SII) 通过测量语音和听力参数来预测可懂度得分。预测的一个组成部分是“重要性函数”，这是一种加权函数，用于描述语音特定谱区对语音可懂度的贡献。先前对听力受损受试者的 SII 预测研究表明，如果可以获得个体受试者的重要性函数，预测准确性可能会提高。不幸的是，先前的重要性函数测量需要使用各种固定带宽低通和高通滤波器对受试者群体进行广泛的可懂度测试。需要一种更适合个体受试者的更有效的方法。本研究旨在评估使用自适应带宽滤波器为个体受试者测量重要性函数的可行性。在两项实验中，十名听力正常的受试者聆听了经过低通和高通滤波器处理的元音-辅音-元音 (VCV) 无意义词，滤波器的带宽根据 Levitt 的转换升降规则自适应变化，以产生指定的性能水平 [(1971)。J. Acoust. Soc. Am. 49, 467-477]。对所得数据进行局部线性心理测量函数拟合，并用于生成 VCV 词的重要性函数。结果表明，自适应方法可靠且高效，并产生与相应 AI/SII 重要性函数一致的重要性函数数据。
