{
  "id": "AIBD-0059",
  "title": "Speaker-tailored neural speech recognition outperforms familiar listeners for a woman with global aphasia and dysarthria (single case)",
  "summary": "A CNN trained only on one patient's voice recognized 72.65% of her words versus 56.75% for 12 familiar professionals; lab result only.",
  "date_observed": "2022-11-01",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
  "source_entry": null,
  "account": "A 34-year-old right-handed woman took part in a single-case proof of concept at the University of Messina and IRCCS Centro Neurolesi Bonino-Pulejo. It appears in Frontiers in Rehabilitation Sciences (2026). She has right hemiparesis, global aphasia and dysarthria from a massive left-hemisphere hemorrhage at age 19. A compact convolutional neural network in the CapisciAMe voice-input communication app was trained only on her voice. Training used 1,120 utterances of 13 Italian target words from 33 sessions in one month in a therapy room. Utterances from November 2022 (day not given, so 1 November is used) were the held-out test set. The tailored recognizer identified 680 of 936 test utterances (72.65%). 12 rehabilitation professionals who knew her averaged 56.75%. No real-world communication use is documented.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "Recognize the impaired speech of a person with aphasia and dysarthria so a communication aid can voice her intended words",
  "system": "CapisciAMe speaker-dependent ASR (compact convolutional neural network, cnn-trad-fpool3) in a voice-input voice-output communication aid",
  "model_version": "cnn-trad-fpool3 trained on 1,120 patient utterances",
  "developer": "University of Messina and IRCCS Centro Neurolesi Bonino-Pulejo research team (CapisciAMe app developed in Italy as a research tool)",
  "deployer": "University of Messina (therapy-room sessions)",
  "location": "Messina, Italy",
  "domain": "Speech-language rehabilitation / augmentative communication",
  "segment": "Adults with post-stroke aphasia and dysarthria",
  "benefit_type": "Improved Task Accuracy",
  "taxonomy_note": "The recognizer is a communication aid for a woman with dysarthria, but only a laboratory accuracy result is documented.",
  "registry_ids": {},
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    {
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      "reason": "Single-case proof of concept with a 13-word vocabulary."
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    "status": "assessed",
    "statement": "The personalized network is the entire intervention. Its 72.65% word accuracy exceeded the 56.75% mean of familiar listeners. The comparison used a 13-word closed set. The authors state the single-case design and limited vocabulary constrain generalization."
  },
  "evidence": [
    {
      "kind": "case report",
      "citation": "University of Messina and IRCCS Centro Neurolesi Bonino-Pulejo authors (2026). Case Report: Tailored automatic speech recognition in global aphasia with dysarthria - a single case proof of concept. Frontiers in Rehabilitation Sciences.",
      "title": "Case Report: Tailored automatic speech recognition in global aphasia with dysarthria - a single case proof of concept",
      "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13271983/",
      "date": null,
      "written_by": "makers",
      "quote": "The tailored ASR correctly identified 680 of 936 test utterances, yielding an overall word-level accuracy of 72.65%"
    },
    {
      "kind": "case report",
      "citation": "Same article (listener benchmark).",
      "title": "Case Report: Tailored automatic speech recognition in global aphasia with dysarthria (listener benchmark)",
      "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13271983/",
      "date": null,
      "written_by": "makers",
      "quote": "Individual listener accuracy ranged from 38.5% to 73.1% (mean = 56.75%, SD = 12.91%)"
    }
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  "replication": {
    "produced_by": [
      "Speaker-dependent training on hundreds of the patient's own utterances",
      "Closed, small target vocabulary",
      "Multiple prompting modalities during recording"
    ],
    "must_hold": [
      "Patient can produce consistent vocalizations for each target word",
      "Quiet recording environment with a headset microphone",
      "Clinician time to collect and label training data"
    ],
    "echoes": []
  },
  "paired_incident": {
    "database": "AI Incident Database",
    "identifier": "732",
    "title": "Whisper Speech-to-Text AI Reportedly Found to Create Violent Hallucinations",
    "url": "https://incidentdatabase.ai/cite/732",
    "severity": null,
    "group": "personal",
    "basis": "Both decode speech for people with impaired speech; Whisper fabricated words and violent phrases when transcribing impaired speech."
  },
  "headline": "CapisciAMe CNN outperforms familiar listeners on impaired speech",
  "metric": "recognized 72.65% of her words versus 56.75% for 12 familiar professionals",
  "comparator": {
    "against": "12 familiar rehabilitation professionals listening to the same utterances",
    "before": "mean = 56.75 %, SD = 12.91",
    "after": "72.65 % accuracy",
    "basis": "measured",
    "quote": "The tailored ASR achieved 72.65 % accuracy, outperforming familiar RPHL (mean = 56.75 %, SD = 12.91).",
    "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13271983/"
  },
  "later": {
    "status": "unknown",
    "date": null,
    "note": "No later development found.",
    "quote": null,
    "url": null
  },
  "value": null,
  "developer_country": "Italy",
  "deployer_country": "Italy",
  "unrated_because": null,
  "brief_notes": null,
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  "revisions": [
    {
      "date": "2026-10-20",
      "change": "Record drafted from the cited sources.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-20",
      "change": "Paired with AI Incident Database incident 732 (high confidence); severity not given by the source.",
      "source": "https://incidentdatabase.ai/cite/732",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-20",
      "change": "Verification sweep: the CapisciAMe case report is written by the app's developers; those sources are now marked as written by the makers. The record is admitted on its peer-reviewed or independent source.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Added what the benefit is measured against from the cited sources.",
      "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13271983/",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.",
      "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
      "source": "https://en.wikipedia.org/wiki/University_of_Messina; https://en.wikipedia.org/wiki/University_of_Messina",
      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-22",
      "change": "Organization name standardized to University of Messina.",
      "source": null,
      "editor": "AIBD"
    }
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  "confidence": "claimed",
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      }
    }
  }
}