Frontiers in rehabilitation sciences · 2026
Case Report: Tailored automatic speech recognition in global aphasia with dysarthria - a single case proof of concept.
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Mulfari Davide, Cardile Davide, Campana Serena, Voci Loredana, Vicario Carmelo Mario, Calabrò Rocco Salvatore, Corallo Francesco, Mulfari Salvatore, Tomaiuolo Francesco
Аннотация
BACKGROUND: Severe dysarthria and global aphasia drastically reduce speech intelligibility, confining communication to familiar partners. Automatic speech recognition (ASR) systems may show limited performance when processing such atypical speech.
OBJECTIVE: To determine whether a speaker-dependent Voice-Input Voice-Output Communication Aid (VIVOCA) embedded in the CapisciAMe app can decode the speech of a person with severe dysarthria and aphasia more accurately than rehabilitation professionals human listeners (RPHL).
METHODS: We conducted a single-case proof-of-concept study. A 34-year-old woman, 15 years post-stroke, recorded 1,120 utterances of 13 target-words across five prompting modalities. A compact convolutional neural network (cnn-trad-fpool3) was trained on these samples and evaluated on an independent set of 936 utterances. Intelligibility was benchmarked against 12 RPHL familiar with the patient. The primary outcome was word-level accuracy.
RESULTS: The tailored ASR achieved 72.65 % accuracy, outperforming familiar RPHL (mean = 56.75 %, SD = 12.91).
CONCLUSIONS: A personalized ASR system can exceed the intelligibility of human listeners for profoundly disordered speech, supporting its use as an assistive communication technology.
OBJECTIVE: To determine whether a speaker-dependent Voice-Input Voice-Output Communication Aid (VIVOCA) embedded in the CapisciAMe app can decode the speech of a person with severe dysarthria and aphasia more accurately than rehabilitation professionals human listeners (RPHL).
METHODS: We conducted a single-case proof-of-concept study. A 34-year-old woman, 15 years post-stroke, recorded 1,120 utterances of 13 target-words across five prompting modalities. A compact convolutional neural network (cnn-trad-fpool3) was trained on these samples and evaluated on an independent set of 936 utterances. Intelligibility was benchmarked against 12 RPHL familiar with the patient. The primary outcome was word-level accuracy.
RESULTS: The tailored ASR achieved 72.65 % accuracy, outperforming familiar RPHL (mean = 56.75 %, SD = 12.91).
CONCLUSIONS: A personalized ASR system can exceed the intelligibility of human listeners for profoundly disordered speech, supporting its use as an assistive communication technology.