SONIVA database: speech recognition validation in aphasia
File(s)
Published version (in press)
Author(s)
Type
Journal Article
Abstract
Post-stroke aphasia is a major contributor to language impairment and neuro-disability worldwide, making automated assessment a critical research priority. However, clinically validated automatic speech recognition (ASR) systems remain limited by the scarcity of large, annotated datasets capturing aphasia’s heterogeneous manifestations. We introduce SONIVA (Speech recOgNItion Validation in Aphasia), the largest and most comprehensively curated database for validating speech recognition in aphasia, comprising audio recordings from approximately 1,000 stroke survivors and 6,000 age-matched controls. The dataset comprises annotated speech from 571 stroke survivors, 103 of whom contributed longitudinal recordings (mean age: 60.65 ± 12.97 years; 68.77% male), and 103 controls (mean age: 59.64 ± 11.48 years; 62.05% male). These recordings are enriched with detailed linguistic coding, orthographic transcriptions, and International Phonetic Alphabet annotations. Foundation models fine-tuned on SONIVA correlate strongly with expert transcriptions (Spearman’s r = 0.79-0.86; p < 0.0001), while acoustic classifiers achieve 93% stroke classification accuracy, enabling scalable analysis for rehabilitation and clinical assessment.
Date Issued
2026-06-13
Date Acceptance
2026-05-29
Citation
Scientific Data, 2026
ISSN
2052-4463
Publisher
Nature Portfolio
Journal / Book Title
Scientific Data
Copyright Statement
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1038/s41597-026-07596-3
Publication Status
Published online
Date Publish Online
2026-06-13