Assessing the intelligibility of speech-disordered individuals generally involves asking them to read aloud texts such as word lists, a procedure that can be time-consuming if the materials are lengthy. This paper seeks to optimize such elicitation materials by identifying an optimal trade-off between the quantity of material needed for assessment purposes and its capacity to elicit a robust intelligibility metrics. More specifically, it investigates the effect of reducing the number of pseudowords used in a phonetic-acoustic decoding task in a speech-impaired population in terms of the subsequent impact on the intelligibility classifier as quantified by accuracy indexes (AUC of ROC, Balanced Accuracy index and F-scores). A comparison of obtained accuracy indexes shows that when reduction of the amount of elicitation material is based on a phonetic criterion—here, related to phonotactic complexity—the classifier has a higher classifying ability than when the material is arbitrarily reduced. Crucially, downsizing the material to about 30% of the original dataset does not diminish the classifier’s performance nor affect its stability. This result is of significant interest to clinicians as well as patients since it validates a tool that is both reliable and efficient.

评估言语障碍者的可理解性通常涉及要求他们大声朗读诸如单词表之类的文本，如果材料很长，这一过程可能会很耗时。本文旨在通过确定评估所需的材料数量与其引出强大的可理解性指标的能力之间的最佳权衡来优化此类启发材料。更具体地说，它研究了在语音障碍人群中减少语音-声学解码任务中使用的伪词数量的效果，这对通过准确度指标（ROC 的 AUC，平衡准确度指数）量化的可理解性分类器的后续影响和 F 分数）。获得的准确度指标的比较表明，当诱导材料的数量减少是基于语音标准时——这里，与语音复杂性有关——分类器比任意减少材料时具有更高的分类能力。至关重要的是，将材料缩小到原始数据集的 30% 左右，不会降低分类器的性能，也不会影响其稳定性。这一结果对临床医生和患者都非常感兴趣，因为它验证了一种既可靠又高效的工具。