View in EDS PDF Full Text

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

Saved in:
Bibliographic Details
Title: ABCD: A Simulation Method for Accelerating Conversational Agents With Applications in Aphasia Therapy.
Authors: Imaezue, Gerald C.1 gimaezue@usf.edu, Marampelly, Harikrishna2
Source: Journal of Speech, Language & Hearing Research. Jul2025, Vol. 68 Issue 7, p3322-3336. 15p.
Subject Terms: *Computer simulation, *Comparative grammar, *Language & languages, *Cost effectiveness, *Conversation, *Data analysis, *Artificial intelligence, *Aphasia, *Communication, *Speech therapy, Scale analysis (Psychology), Speech, Natural language processing, Descriptive statistics, Content mining, Statistics, Semantics, Data analysis software, Chatbots
Abstract: 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Speech, Language & Hearing Research is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: ehh
DbLabel: Education Research Complete
An: 186522589
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: ABCD: A Simulation Method for Accelerating Conversational Agents With Applications in Aphasia Therapy.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Imaezue%2C+Gerald+C%2E%22">Imaezue, Gerald C.</searchLink><relatesTo>1</relatesTo><i> gimaezue@usf.edu</i><br /><searchLink fieldCode="AR" term="%22Marampelly%2C+Harikrishna%22">Marampelly, Harikrishna</searchLink><relatesTo>2</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Speech%2C+Language+%26+Hearing+Research%22">Journal of Speech, Language & Hearing Research</searchLink>. Jul2025, Vol. 68 Issue 7, p3322-3336. 15p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br />*<searchLink fieldCode="DE" term="%22Comparative+grammar%22">Comparative grammar</searchLink><br />*<searchLink fieldCode="DE" term="%22Language+%26+languages%22">Language & languages</searchLink><br />*<searchLink fieldCode="DE" term="%22Cost+effectiveness%22">Cost effectiveness</searchLink><br />*<searchLink fieldCode="DE" term="%22Conversation%22">Conversation</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Aphasia%22">Aphasia</searchLink><br />*<searchLink fieldCode="DE" term="%22Communication%22">Communication</searchLink><br />*<searchLink fieldCode="DE" term="%22Speech+therapy%22">Speech therapy</searchLink><br /><searchLink fieldCode="DE" term="%22Scale+analysis+%28Psychology%29%22">Scale analysis (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Speech%22">Speech</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Content+mining%22">Content mining</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Chatbots%22">Chatbots</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Speech, Language & Hearing Research is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=186522589
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1044/2025_JSLHR-25-00003
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 3322
    Subjects:
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Comparative grammar
        Type: general
      – SubjectFull: Language & languages
        Type: general
      – SubjectFull: Cost effectiveness
        Type: general
      – SubjectFull: Conversation
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Aphasia
        Type: general
      – SubjectFull: Communication
        Type: general
      – SubjectFull: Speech therapy
        Type: general
      – SubjectFull: Scale analysis (Psychology)
        Type: general
      – SubjectFull: Speech
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Content mining
        Type: general
      – SubjectFull: Statistics
        Type: general
      – SubjectFull: Semantics
        Type: general
      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Chatbots
        Type: general
    Titles:
      – TitleFull: ABCD: A Simulation Method for Accelerating Conversational Agents With Applications in Aphasia Therapy.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Imaezue, Gerald C.
      – PersonEntity:
          Name:
            NameFull: Marampelly, Harikrishna
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 10924388
          Numbering:
            – Type: volume
              Value: 68
            – Type: issue
              Value: 7
          Titles:
            – TitleFull: Journal of Speech, Language & Hearing Research
              Type: main
ResultId 1