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Sketchpad Series

Mirror Speech Entrainment: A novel technique for entraining speech production in aphasia

Poster Session E - Monday, March 31, 2025, 2:30 – 4:30 pm EDT, Back Bay Ballroom/Republic Ballroom

Celine Davis1 (celinedavis@usf.edu), Gerald C. Imaezue1; 1University of South Florida

Nonfluent aphasia, often caused by stroke, is characterized by effortful, fragmented speech. Speech entrainment, which uses auditory (voice-speech) or audiovisual stimuli (facial mouth movements plus speech), has shown promise in entraining and improving speech fluency in persons with nonfluent aphasia (PWNA). Traditional speech entrainment relies on external stimuli; however, research suggests individuals focus more on their voice (self-voice attention) or face (self-face attention) than on external sources. Building on this, we introduce Mirror Speech Entrainment (MSE), a novel approach leveraging AI-generated personalized stimuli—voice and video clones of participants—to entrain PWNA’s speech using their own voice (audio) and voice paired with facial movements (video). Three feasibility studies, each employing a single-arm (within-subjects) controlled design, will test MSE. In Study 1, four English-speaking PWA (>6 months poststroke) sequentially receive MSE-audio and MSE-video treatments, counterbalanced across participants. Speech rate and accuracy are assessed at baseline (T1), post-treatment one (T2), and post-treatment two (T3). Study 2 evaluates four additional PWA using MSE-video versus traditional audiovisual entrainment, with counterbalanced treatment orders and identical assessment timepoints. Study 3 compares MSE-audio with traditional audio entrainment using the same protocol. All treatments are delivered via mobile apps that automate both MSE and traditional entrainment procedures. Pre- and post-treatment assessments will evaluate gains in speech rate and accuracy in PWNA. Findings aim to determine the feasibility of MSE as an innovative tool for improving speech production in PWNA.

Topic Area: LANGUAGE: Other

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