Chatphasia: a personalized end-to-end system for aphasia therapy

This paper introduces an AI-powered system designed to revolutionize therapy for aphasia patients by enhancing accessibility and personalization. The solution integrates a mobile application for patients to perform word-retrieval tasks and a web dashboard for practitioners to monitor progress and tailor therapy. Key components include a fine-tuned Automatic Speech Recognition (ASR) model optimized for Singaporean aphasic speech and a Large Language Model (LLM)-driven cue generation framework based on hierarchical techniques used in speech therapy. The combination of these components forges a comprehensive tool for speech rehabilitation, demonstrating significant improvements in usability, transcription accuracy, and therapy outcomes.

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Publication Details

Published
2025-09-22
DOI
https://doi.org/10.1117/12.3077156
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00
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Chatphasia: a personalized end-to-end system for aphasia therapy

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EEG and Brain-Computer Interfaces
article

Chatphasia: a personalized end-to-end system for aphasia therapy

Xin He, Farhan Azmi, Han Xiang Kee, Ying Zhen Lee, Wen Qin Yeo, Tong Rong
article en

Abstract

This paper introduces an AI-powered system designed to revolutionize therapy for aphasia patients by enhancing accessibility and personalization. The solution integrates a mobile application for patients to perform word-retrieval tasks and a web dashboard for practitioners to monitor progress and tailor therapy. Key components include a fine-tuned Automatic Speech Recognition (ASR) model optimized for Singaporean aphasic speech and a Large Language Model (LLM)-driven cue generation framework based on hierarchical techniques used in speech therapy. The combination of these components forges a comprehensive tool for speech rehabilitation, demonstrating significant improvements in usability, transcription accuracy, and therapy outcomes.

Singapore Institute of Technology (SG)
Openalex Percentile: Top 74%
EEG and Brain-Computer Interfaces
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