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SONIVA database: Speech recognition validation in aphasia.

Giulia Sanguedolce1,2,3, Cathy J Price4, Sophie Brook3

  • 1Department of Computing, Imperial College London, London, UK.

Scientific Data
|June 13, 2026
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Summary
This summary is machine-generated.

A new large dataset, SONIVA, enables better automated speech recognition for post-stroke aphasia. This aids in developing accurate tools for language impairment assessment and rehabilitation.

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Area of Science:

  • Neuroscience
  • Computational Linguistics
  • Speech Technology

Background:

  • Post-stroke aphasia causes significant language impairment and disability.
  • Automated assessment of aphasia is crucial but hindered by limited annotated speech data.
  • Existing automatic speech recognition (ASR) systems struggle with the diverse nature of aphasia.

Purpose of the Study:

  • To introduce SONIVA (Speech recOgNItion Validation in Aphasia), the largest annotated speech dataset for validating ASR in aphasia.
  • To provide a resource for developing and evaluating ASR systems tailored to individuals with post-stroke aphasia.
  • To facilitate advancements in automated language assessment and rehabilitation tools for aphasia.

Main Methods:

  • Collected audio recordings from approximately 1,000 stroke survivors and 6,000 age-matched controls.
  • Annotated speech data from 571 stroke survivors with linguistic coding, orthographic transcriptions, and IPA.
  • Fine-tuned foundation models and trained acoustic classifiers on the SONIVA dataset.

Main Results:

  • Foundation models fine-tuned on SONIVA showed strong correlation with expert transcriptions (Spearman's r = 0.79-0.86).
  • Acoustic classifiers achieved 93% accuracy in classifying stroke survivors.
  • The SONIVA dataset enables scalable analysis for aphasia assessment and rehabilitation.

Conclusions:

  • The SONIVA dataset is a significant resource for advancing ASR in post-stroke aphasia.
  • Accurate ASR systems can be developed using SONIVA, improving language impairment assessment.
  • This work paves the way for more effective clinical tools and rehabilitation strategies for aphasia patients.