Background: Imprecise articulation has a negative impact on speech intelligibility. Therefore, treatment of articulation is clinically relevant in patients with dysarthria. In order to be effective and according to the principles of motor learning, articulation therapy needs to be intensive, well organized, with adequate feedback and requires frequent practice. Aims:The aims of this pilot study are (1) to evaluate the feasibility of a virtual articulation therapy (VAT) to guide patients with dysarthria through a boost articulation therapy (BArT) program; (2) to evaluate the acoustic models' performance used for automatic phonological error detection; and (3) to validate the system by end-users from their perspective. Methods & Procedures:The VAT provides an extensive and well-structured package of exercises with visual and auditory modelling and adequate feedback on the utterances. The tool incorporates automated methods to detect phonological errors, which are specifically designed to analyse Dutch speech production. A total of 14 subjects with dysarthria evaluated the acceptability, usability and user interaction with the VAT based on two completed therapy sessions using a self-designed questionnaire. Outcomes & Results:In general, participants were positive about the new computer-based therapy approach. The algorithm performance for phonological error detection shows it to be accurate, which contributes to adequate feedback of utterance production. The results of the study indicate that the VAT has a userfriendly interface that can be used independently by patients with dysarthria who have sufficient cognitive, linguistic, motoric and sensory skills to benefit from speech therapy. Recommendations were given by the end-users to further optimize the program and to ensure user engagement. Conclusions & Implications:The initial implementation of an automatic BArT shows it to be feasible and well accepted by end-users. The tool is an appropriate solution to increase the frequency and intensity of articulation training that supports traditional methods.
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