Benchmarking Automatic Speech Recognition for Aphasia: A Clinical Evaluation Framework

Abstract

AI tools for speech therapy represent more than innovation; they signify necessity. With 89% of clinicians facing overwhelming caseloads and therapy wait times averaging 3-6 months, the demand for scalable rehabilitation support has never been higher. Although AI-driven communication platforms increasingly provide real-time feedback and sustained engagement, current Automatic Speech Recognition (ASR) systems still face significant limitations in accurately processing disordered speech such as aphasia. Aphasia is an acquired language disorder, the speech of individuals with aphasia is characterized by unintelligible words, jargon, or non-words. To add to this the speaker with aphasia may not recognize their errors and mostly have difficulty in comprehension. These limitations hinder equitable access to AI-driven rehabilitation tools. To bridge this gap, the first contribution of this study is evaluating four state-of-the-art ASR models, such as Whisper, NeMo-Conformer, Wav2Vec 2.0, and SpeechBrain, through the lens of Speech-Language Pathologist (SLP). The second contribution of the paper is utilizing a comprehensive benchmarking framework to assess how effectively these models capture clinically relevant aspects of aphasic speech, including lexical, syntactic, and fluency-related features. For evaluating the transcribed text, a combination of quantitative, qualitative (human-expert based), and linguistically grounded evaluation metrics is used, such as verb error rate, noun error rate, mean dependency length etc. of transcribed text.

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