S-DiverSe: Spanish Diverse Speech
- URL: http://arxiv.org/abs/2607.03207v1
- Date: Fri, 03 Jul 2026 11:23:43 GMT
- Title: S-DiverSe: Spanish Diverse Speech
- Abstract summary: S-DiverSe is a corpus of 3.2 hours of in-the-wild Spanish speech from 22 speakers with amyotrophic lateral sclerosis, Parkinson's disease, and stroke.<n>The dataset contains 444 manually transcribed audio segments with metadata on speaker sex, disease type, and intelligibility.
- Score: 67.47251829943015
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- ESCUCHA: A Spanish Speech Benchmark for Heterogeneous Acoustic Conditions [66.93528077633728]
ESCUCHA comprises 1,000 human-curated questions paired with audio, totaling 162.9 hours sourced directly from the wild'' rather than drawn from existing datasets.<n>The benchmark emphasizes reasoning, spanning 9 perceptual and 10 reasoning categories, and it captures linguistic diversity through multiple Spanish accents and non-normative speech.<n> Benchmarking several state-of-the-art multimodal and speech models reveals substantial performance gaps relative to trained humans.
arXiv Detail & Related papers (2026-07-20T10:59:15Z) - MUSCAT: MUltilingual, SCientific ConversATion Benchmark [60.87925076316812]
The goal of multilingual speech technology is to facilitate seamless communication between individuals speaking different languages.<n>To create this experience, speech technology needs to address several challenges: Handling mixed multilingual input, specific vocabulary, and code-switching.<n>We propose a new benchmark to evaluate current Automatic Speech Recognition (ASR) systems, whether they are able to handle these challenges.
arXiv Detail & Related papers (2026-04-17T10:39:01Z) - Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease [72.0406069194794]
Speech representations often encode language-dependent structure that can confound dysarthria detection.<n>We propose a representation-level language shift that aligns source-language self-supervised speech representations with the target-language distribution.<n>We evaluate the approach on oral DDK recordings from Parkinson's disease speech datasets in Czech, German, and Spanish.
arXiv Detail & Related papers (2026-03-23T17:23:39Z) - HISPASpoof: A New Dataset For Spanish Speech Forensics [11.197805021913567]
HISPASpoof is the first large-scale Spanish dataset designed for synthetic speech detection and attribution.<n>It includes real speech from public corpora across six accents and synthetic speech generated with six zero-shot TTS systems.<n>We evaluate five representative methods, showing that detectors trained on English fail to generalize to Spanish.
arXiv Detail & Related papers (2025-09-11T05:29:07Z) - Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource Languages [49.31519786009296]
We fine-tune a voice conversion model on English dysarthric speech (UASpeech) to encode both speaker characteristics and prosodic distortions.<n>We then apply it to convert healthy non-English speech (FLEURS) into non-English dysarthric-like speech.<n>The generated data is then used to fine-tune a multilingual ASR model, Massively Multilingually Speech (MMS)
arXiv Detail & Related papers (2025-05-20T20:03:45Z) - NeuroVoz: a Castillian Spanish corpus of parkinsonian speech [34.916222066004465]
This manuscript presents the NeuroVoz corpus consisting of 112 native Castilian-Spanish speakers, including 58 healthy controls and 54 individuals with PD.<n>The dataset is also complemented with subjective assessments of voice quality performed by an expert according to the GRBAS scale.<n>This data set has already supported several studies, achieving a benchmark accuracy of 89% for the screening of PD.
arXiv Detail & Related papers (2024-03-04T16:17:39Z) - Joint Pre-Training with Speech and Bilingual Text for Direct Speech to
Speech Translation [94.80029087828888]
Direct speech-to-speech translation (S2ST) is an attractive research topic with many advantages compared to cascaded S2ST.
Direct S2ST suffers from the data scarcity problem because the corpora from speech of the source language to speech of the target language are very rare.
We propose in this paper a Speech2S model, which is jointly pre-trained with unpaired speech and bilingual text data for direct speech-to-speech translation tasks.
arXiv Detail & Related papers (2022-10-31T02:55:51Z) - Cross-lingual Self-Supervised Speech Representations for Improved
Dysarthric Speech Recognition [15.136348385992047]
This study explores the usefulness of using Wav2Vec self-supervised speech representations as features for training an ASR system for dysarthric speech.
We train an acoustic model with features extracted from Wav2Vec, Hubert, and the cross-lingual XLSR model.
Results suggest that speech representations pretrained on large unlabelled data can improve word error rate (WER) performance.
arXiv Detail & Related papers (2022-04-04T17:36:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.