ABCD: A Simulation Method for Accelerating Conversational Agents With Applications in Aphasia Therapy.

purposeDevelopment of aphasia therapies is limited by clinician shortages, patient recruitment challenges, and funding constraints. To address these barriers, we introduce

methodBuilt on OpenAI's GPT-4o as the foundational large language model, Re-Agent and AI-Aphasic were supplemented with external speech-to-text and naturalistic text-to-speech application programming interfaces to create a multiturn, dynamic dialogue system in English. We used it to evaluate Re-Agent's conversational performance across four experimental conditions (CoT + picture, CoT + topic, zero-shot + picture, zero-shot + topic) and aphasic error at two levels: word and discourse errors. Re-Agent's performance was measured using three discourse metrics: global coherence, local coherence, and grammaticality of utterances.

resultsOverall, Re-Agent performed accurately in all the discourse metrics across all levels of semantic parameter, prompting technique and aphasic error. The results also indicated that well-crafted zero-shot prompts induce more direct and logically related responses that are robust to adversarial aphasic speech inputs, whereas CoT might lead to responses that slightly lose local coherence due to additional complex reasoning chains.

conclusionsABCD represents a foundational computational approach to accelerate the innovation and preclinical testing of conversational AI partners for speech-language therapy. ABCD circumvents the barriers of collecting diverse errorful speech samples for clinical conversational AI fine-tuning. As AI systems-including large language models and speech technologies-advance rapidly, ABCD will scale accordingly, further enhancing its potential for clinical integration.