Bridging neuroscience and AI: adaptive, culturally sensitive technologies transforming aphasia rehabilitation
Organizations: A*STAR Institute for Infocomm Research (I2R)
Abstract
Aphasia, a language impairment primarily resulting from stroke or brain injury, profoundly disrupts communication and everyday functioning. Despite advances in speech therapy, barriers such as limited therapist availability and the scarcity of personalized, culturally relevant tools continue to hinder optimal rehabilitation outcomes. This paper reviews recent developments in neurocognitive research and language technologies that contribute to the diagnosis and therapy of aphasia. Drawing on findings from our ethnographic field study, we introduce two digital therapy prototypes designed to reflect local linguistic diversity and enhance patient engagement. We also show how insights from neuroscience and the local context guided the design of these tools to better meet patient and therapist needs. Our work highlights the potential of adaptive, AI-enhanced assistive technologies to complement conventional therapy and broaden access to therapy. We conclude by outlining future research directions for advancing personalized and scalable aphasia rehabilitation.
Figures & tables
| Neurolinguistic insights | NLP applications | Speech therapy goals | Prototype implementation |
| 1. Knowledge of syntactic deficits | Use parsers to detect malformed sentences | Sentence-level training | Prototype 2: ’Complete the Sentence’ targets syntax via sentence construction (1:1 match) |
| 2. Lexical-semantic mapping in the brain | Semantic vector models | Suggest related words during naming | Planned: Future versions of both prototypes may use LLMs to offer flexible suggestions |
| 3. Multimodal associa- tion in lexical access | Vision-language models | Use image cues to trigger speech | Prototype 1: Current 1:1 image - word pronunciation match; visual stimuli planned |
| 4. Real-time language processing deficits | ERP-based dialogue pacing | Adapt turn-taking pace | Planned: Potential for future interaction design |
| 5. Pronunciation errors | Classification models on aphasic speech | Error-specific feedback | Prototype 1: Custom scoring + DTW detect phonetic errors, give personalized feedback |
| 6. Reduced speech fluency & word retrieval | Rhythmic/formulaic language models | Support automaticity; use fixed patterns | Planned: Singing-based or patterned repetition exercises may be added |
Healthcare
Healthcare AI develops and evaluates models and systems for clinical care, medical research, public health, and healthcare operations.
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