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Using GPT-4o with speech-to-text and text-to-speech interfaces, Re-Agent is evaluated across semantic constraints and error types via discourse metrics, showing robust zero-shot prompt benefits.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/abcd-a-simulation-method-for-accelerating-conversational-agents-with-applications-in-aphasia-therapy/405557/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/abcd-a-simulation-method-for-accelerating-conversational-agents-with-applications-in-aphasia-therapy/405557.png","ImageObject",300,407,{"name":42,"@type":43},"Eliana","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-09-28",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does ABCD aim to solve in aphasia therapy development?","Question",{"text":63,"@type":64},"ABCD targets barriers including clinician shortages, challenges recruiting patients for clinical studies, and constraints on funding.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does ABCD simulate aphasia care conversations?",{"text":68,"@type":64},"ABCD uses Agent-Based Conversational Dialogue between an AI clinician (Re-Agent) and an AI-Aphasic agent that vocally mimics aphasic errors in multi-turn spoken dialogues.",{"name":70,"@type":61,"acceptedAnswer":71},"What evaluation results does the study report about Re-Agent?",{"text":72,"@type":64},"Re-Agent performs accurately across discourse metrics across semantic parameters and aphasic error levels; zero-shot prompts produce more direct, logically related responses and are robust to adversarial aphasic speech, while CoT may slightly reduce local coherence.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},405557,1790742805,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145,"preview_page_count":4},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","| Research Article\u003Cbr>ABCD: A Simulation Method for Accelerating Conversational Agents With Applications in Aphasia Therapy\u003Cbr>Gerald C. Imaezuea  and Harikrishna Marampellyb\u003Cbr>a Department of Communication Sciences and Disorders, University of South Florida, Tampa b Department of Computer Science and Engineering, University of South Florida, Tampa |  |\n| --- | --- |\n| A R T I C L E I N F O\u003Cbr>Article History:\u003Cbr>Received January 3, 2025\u003Cbr>Revision received March 21, 2025\u003Cbr>Accepted April 14, 2025\u003Cbr>Editor-in-Chief: Julie A. Washington\u003Cbr>Editor: Amy D. Rodriguez\u003Cbr>[https://doi.org/10.1044/2025_JSLHR-25-00003](https://doi.org/10.1044/2025_JSLHR-25-00003) | A B S T R A C T |\n|  | Purpose: Development of aphasia therapies is limited by clinician shortages, patient recruitment challenges, and funding constraints. To address these barriers, we introduce Agent-Based Conversational Dialogue (ABCD), a novel method for simulating goal-driven natural spoken dialogues between two conversational artificial intelligence (AI) agents—AI clinician (Re-Agent) and AI patient (AI-Aphasic), which vocally mimics aphasic errors. Using ABCD, we simulated response elaboration training between both agents with stimuli varying in semantic constraint (high via pictures, low via topics) . Rather than resource-intensive finetuning, we leveraged prompt engineering, chain-of-thought (CoT) and zero-shot techniques for rapid, cost-effective agent development, and piloting.\u003Cbr>Method: Built 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.\u003Cbr>Results: Overall, 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.\u003Cbr>Conclusions: ABCD represents a foundational computational approach to accelerate the innovation and preclinical testing of conversational AI partners for speechlanguage 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. |\n\nPatients with neurological conditions such as stroke often develop aphasia, a language disorder characterized by fragmented and constrained speech outputs, significantly impacting quality of life. Evidence-based therapies,  \nCorrespondence to Gerald C. Imaezue: [gimaezue@usf.edu.](gimaezue@usf.edu. Disclo)[ Disclo](gimaezue@usf.edu. Disclo)sure: The authors have declared that no competing financial or nonfinancial interests existed at the time of publication.  \nsuch as response elaboration training (RET), have been shown to enable communicative functions in people with aphasia (PWA) by facilitating the expansion of verbal responses during picture description through a chaining technique (Kearns, 1985) . RET involves prompting PWA to elaborate on a picture or video (high-semantic constraint), thereby increasing the length and complexity of their utterances over time (e.g., Ahmadi et al., 2022; Bunker et al., 2019; Wambaugh & Martinez, 2000). However, the dev","cbCaij1AJtB95ETy","https://ap.wps.com/l/cbCaij1AJtB95ETy","pdf",593792,15,"English","# Abstract\n## Purpose\n## Method\n## Results\n## Conclusions","[{\"question\":\"What problem does ABCD aim to solve in aphasia therapy development?\",\"answer\":\"ABCD targets barriers including clinician shortages, challenges recruiting patients for clinical studies, and constraints on funding.\"},{\"question\":\"How does ABCD simulate aphasia care conversations?\",\"answer\":\"ABCD uses Agent-Based Conversational Dialogue between an AI clinician (Re-Agent) and an AI-Aphasic agent that vocally mimics aphasic errors in multi-turn spoken dialogues.\"},{\"question\":\"What evaluation results does the study report about Re-Agent?\",\"answer\":\"Re-Agent performs accurately across discourse metrics across semantic parameters and aphasic error levels; zero-shot prompts produce more direct, logically related responses and are robust to adversarial aphasic speech, while CoT may slightly reduce local coherence.\"}]","ABCD: A Simulation Method for Accelerating Conversational Agents With Applications in Aphasia Therapy | PDF",1790560554,38]