S-DiverSe: Spanish Diverse Speech
Provides a dedicated benchmark for ASR on neurologically affected Spanish speech, a previously underexplored area.
S-DiverSe introduces a 3.2-hour Spanish speech corpus from 22 speakers with neurological conditions, finding that heuristic text post-processing outperforms fine-tuning for ASR on such out-of-domain speech.
Automatic speech recognition (ASR) has advanced remarkably for standard speech, yet speech affected by neurological conditions remains a challenge. We present S-DiverSe (Spanish Diverse Speech), a corpus of 3.2 hours of in-the-wild Spanish speech from 22 speakers with amyotrophic lateral sclerosis, Parkinson's disease, and stroke. The dataset contains 444 manually transcribed audio segments with metadata on speaker sex, disease type, and intelligibility. S-DiverSe is designed to support ASR evaluation and development for neurologically affected Spanish speech. We describe the dataset, analyze its composition, and report baseline ASR results alongside initial adaptation experiments. Our findings reveal that heuristic text post-processing is more robust than fine-tuning for out-of-domain neurological Spanish speech. This underscores the need for dedicated in-the-wild Spanish benchmarks.