  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.
