Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data

• URL: http://arxiv.org/abs/2308.04763v1
• Date: Wed, 9 Aug 2023 07:51:40 GMT
• Title: Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data
• Authors: Lionel Fontan, Typhanie Prince (Praxiling, LNPL), Aleksandra Nowakowska (Praxiling), Halima Sahraoui (LNPL), Silvia Martinez-Ferreiro
• Abstract summary: 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.
• Score: 55.84746218227712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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