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dc.contributor.authorFontan, Lionel
dc.contributor.authorPrince, Typhanie
dc.contributor.authorNowakowska, Aleksandra
dc.contributor.authorSahraoui, Halima
dc.contributor.authorMartínez-Ferreiro, Silvia
dc.date.accessioned2023-11-16T09:05:14Z
dc.date.available2023-11-16T09:05:14Z
dc.date.issued2023-08-07
dc.identifier.citationFontan L, Prince T, Nowakowska A, Sahraoui H, Martínez-Ferreiro S. Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data. Aphasiology. 2024;38:939-956.es_ES
dc.identifier.issn0268-7038
dc.identifier.urihttp://hdl.handle.net/2183/34264
dc.description.abstract[Abstract] Background. Speech and language pathologists (SLPs) often rely on judgements of speech fluency for diagnosing or monitoring patients with aphasia. However, such subjective methods have been criticised for their lack of reliability and their clinical cost in terms of time. Aims. This study aims at assessing the relevance of a signal-processing algorithm, 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 participants were recruited via non-profit organizations and SLP networks. All participants were recorded while reading out loud a set of sentences 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 multiple linear 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.es_ES
dc.description.sponsorshipThe study was funded by the European Regional Development Fund (ERDF), within the framework of the research project “Aphasie et Discours en Interaction (AADI) [Aphasia And Discourse in Interaction (AADI)]” (funding number: 2019-A03105-52). SMF also acknowledges support of Ramón y Cajal (RYC2020- 028927-1), Ministerio de Ciencia e Innovación, Spain.es_ES
dc.description.sponsorshipMinisteriod e Ciencia e Innovación (España); RYC2020-028927-1
dc.language.isoenges_ES
dc.publisherTaylor & Francises_ES
dc.relation.urihttps://doi.org/10.1080/02687038.2023.2244728es_ES
dc.rightsThis is an original manuscript of an article published by Taylor & Francis in Aphasiology on 2023.es_ES
dc.subjectSpeech fluencyes_ES
dc.subjectAutomatic assessmentes_ES
dc.subjectAphasiaes_ES
dc.titleAutomatically measuring speech fluency in people with aphasia: first achievements using read-speech dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleAphasiologyes_ES


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