ArticleScientific data2026
HeyJay! A corpus of atypical speech for spoken language understanding and automatic speech recognition.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- HeyJay! A corpus of atypical speech for spoken language understanding and automatic speech recognition.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Speech technologies, such as automatic speech recognition or spoken language understanding, are not usually adapted to atypical speech, i.e., the speech of people with dysarthria, dysphonia, or another type of speech impairment. That prevents atypical speakers from leveraging speech assistants or other human-machine-interaction-powered platforms, which could make their lives easier or increase their independence. In this article, we present HeyJay!, a new corpus of atypical speech in English language from participants with neurodegenerative disorders, including Parkinson's Disease, or Amyotrophic Lateral Sclerosis. The current corpus version comprises 8,669 utterance recordings, including supervised transcriptions and intent annotations. In this study, we demonstrate the validity of the corpus by applying it to automatic speech recognition, spoken language understanding, and data augmentation tasks. Additionally, the dataset includes speech quality ratings for each participant, performed by expert speech and language pathologists. This corpus, the first one with intent annotation of atypical speech that is publicly available, is intended to create more fair speech technologies for atypical speakers by adapting and improving the state of the art, and to facilitate further research in the field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.