SONIVA database: Speech recognition validation in aphasia

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.