Background: Speech and language pathologists (SLPs) often relyon judgements of speech fluency for diagnosing or monitoringpatients with aphasia. However, such subjective methods havebeen criticised for their lack of reliability and their clinical cost interms of time. Aims: This study aims at assessing the relevance of a signalprocessingalgorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency in people with aphasia (PWA). Methods & Procedures: Twenty-nine PWA and five control participantswere recruited via non-profit organizations and SLP networks. All participants were recorded while reading out loud a set ofsentences taken from the French version of the Boston Diagnostic Aphasia Examination. Three trained SLPs assessed the fluency of each sentence on a five-point qualitative scale. A forward-backward divergence segmentation and a clustering algorithm were used to compute, for each sentence, four automatic predictors of speech fluency: pseudo-syllable rate, speech ratio, rate of silent breaks, and standard deviation of pseudo-syllable length. The four predictors were finally combined into multivariate regression models (a multiplelinear regression - MLR, and two non-linear models) to predict the average SLP ratings of speech fluency, using a leave-one speaker-out validation scheme. Outcomes & Results: All models achieved accurate predictions of speech fluency ratings, with average root-mean-square errors as low as 0.5. The MLR yielded a correlation coefficient of 0.87 with reference ratings at the sentence level, and of 0.93 when aggregating the data for each participant. The inclusion of an additional predictor sensitive to repetitions improved further the predictions with a correlation coefficient of 0.91 at the sentence level, and of 0.96 at the participant level. Conclusions: The algorithms used in this study can constitute a cost-effective and reliable tool for the assessment of the speech fluency of patients with aphasia in read-aloud tasks. Perspectives for the assessment of spontaneous speech are discussed.

翻译：背景：言语病理学家常依赖对言语流畅性的主观判断来诊断或监测失语症患者。然而，此类主观方法因缺乏可靠性及临床时间成本较高而受到批评。目的：本研究旨在评估一种最初应用于语言习得领域的信号处理算法在自动测量失语症患者言语流畅性方面的适用性。方法与流程：通过非营利组织及言语病理学家网络招募了29名失语症患者和5名对照参与者。所有参与者需朗读取自《波士顿失语症诊断测验》法语版的若干句子，并由三位受过训练的言语病理学家采用五级定性量表对每句话的流畅性进行评估。采用前向-后向发散分割法与聚类算法，为每句话计算四个自动预测变量：伪音节率、言语比率、无声停顿率及伪音节时长标准差。最后，通过多元回归模型（包括多元线性回归及两种非线性模型）整合四个预测变量，采用留一说话人交叉验证法，预测言语流畅性的平均言语病理学家评分。结果：所有模型均能准确预测言语流畅性评分，平均均方根误差低至0.5。在句子层面，多元线性回归模型与参考评分的相关系数为0.87；在汇总每位参与者数据后，相关系数升至0.93。引入一个对重复现象敏感的额外预测变量后，模型在句子层面相关系数提升至0.91，在参与者层面达0.96。结论：本研究所用算法可构成一种经济高效的可靠工具，用于评估失语症患者在朗读任务中的言语流畅性。本研究同时探讨了对自发性言语进行评估的前景。