# S-DiverSe: Spanish Diverse Speech

arXiv: https://arxiv.org/abs/2607.03207 · HTML: https://mlsanity.com/paper/2607.03207/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Ministral 3](https://mlsanity.com/paper/2601.08584/) (2026)
- [Voxtral](https://mlsanity.com/paper/2507.13264/) (2025)
- [Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages](https://mlsanity.com/paper/2511.09690/) (2025)
- [NeuroVoz: a Castillian Spanish corpus of parkinsonian speech](https://mlsanity.com/paper/2403.02371/) (2024)
- [Salamandra Technical Report](https://mlsanity.com/paper/2502.08489/) (2025)
- [The Interspeech 2025 Speech Accessibility Project Challenge](https://mlsanity.com/paper/2507.22047/) (2025)
- [ASR Benchmarking: Need for a More Representative Conversational Dataset](https://mlsanity.com/paper/2409.12042/) (2024)

## Abstract (arXiv)

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.