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A multimodal speech-production dataset with time-aligned articulography, EEG, audio, and vocal-tract anatomy.

Created on 03 Jul 2026

Authors

Daniel Friedrichs, Valeriia Vyshnevetska, Monica Lancheros, Elena Bolt, Volker Dellwo, Steven Moran

Published in

Scientific data. Jul 01, 2026. Epub Jul 01, 2026.

Abstract

We present a multimodal speech-production dataset combining simultaneous electromagnetic articulography (EMA), electroencephalography (EEG), and audio from 29 adult native speakers of German. All participants have external craniofacial anthropometry; an anatomical subset (N = 18) additionally contributed acoustic pharyngometry, rhinometry, and 3D head surface meshes. Speech materials include high-trial diadochokinetic sequences at habitual and maximally fast rates, plus EMA+audio for passage reading and sustained vowels, as well as EMA for palate tracing and non-speech oromotor actions. The corpus contains 8,700 syllable-task trials and approximately 17 h of EMA with matched audio. A microcontroller mirrors each EMA sweep's start and stop as 1 ms transistor-transistor logic (TTL) pulses on the EEG digital Status channel, enabling sub-millisecond alignment. We distribute raw and minimally processed streams, stable event codes, and machine-readable metadata, plus example Python utilities (and a container) for loading, synchronising, and basic preprocessing. The resource supports studies that exploit articulatory landmarks for EEG alignment, examine pre-movement activity, assess overt-speech EEG artefact handling, and develop anatomy-informed models linking vocal-tract structure to articulatory dynamics and acoustics. Data and code are openly available under a CC-BY 4.0 licence with versioned DOIs.

PMID:
42393090
Bibliographic data and abstract were imported from PubMed on 03 Jul 2026.

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