Evidence map›Paper›PMID 42236740›Full record

ArticleScientific data2026

HeyJay! A corpus of atypical speech for spoken language understanding and automatic speech recognition.

Laureano Moro-Velazquez, Helin Wang, Alison Gunzler, Milind Rao, Ankur A Butala, Lora Clawson, Venkatesh Ravichandran

Abstract readDataset
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Laureano Moro-VelazquezJohns Hopkins University, Dept. of Electrical and Computer Engineering, Baltimore, 21218, USA. laureano@jhu.edu.
Helin WangJohns Hopkins University, Dept. of Electrical and Computer Engineering, Baltimore, 21218, USA.
Alison GunzlerJohns Hopkins University, Dept. of Electrical and Computer Engineering, Baltimore, 21218, USA.
Milind RaoAmazon Inc., Seattle, 98109, USA.
Ankur A ButalaJohns Hopkins University School of Medicine, Dept. of Neurology, Baltimore, 21205, USA.
Lora ClawsonJohns Hopkins University School of Medicine, Dept. of Neurology, Baltimore, 21205, USA.
Venkatesh RavichandranAmazon Inc., Seattle, 98109, USA.

Funding

JHU+Amazon ai2ai faculty award No grant number
6 · The paper itself

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

SpeechSpeech Recognition SoftwareAmyotrophic Lateral SclerosisDysarthriaHumansParkinson Disease

Identifiers

PMID42236740
PMCPMC13538537

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.