A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images
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Published:2021-07-20
Issue:1
Volume:8
Page:
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ISSN:2052-4463
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Container-title:Scientific Data
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language:en
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Short-container-title:Sci Data
Author:
Lim YongwanORCID, Toutios AsteriosORCID, Bliesener Yannick, Tian YeORCID, Lingala Sajan Goud, Vaz Colin, Sorensen TannerORCID, Oh MiranORCID, Harper SarahORCID, Chen WeiyiORCID, Lee YoonjeongORCID, Töger JohannesORCID, Monteserin Mairym Lloréns, Smith Caitlin, Godinez Bianca, Goldstein Louis, Byrd DaniORCID, Nayak Krishna S.ORCID, Narayanan Shrikanth S.ORCID
Abstract
AbstractReal-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open dataset providing raw multi-coil RT-MRI data from an optimized speech production experimental setup. Such datasets could enable new and improved methods for dynamic image reconstruction, artifact correction, feature extraction, and direct extraction of linguistically-relevant biomarkers. The present dataset offers a unique corpus of 2D sagittal-view RT-MRI videos along with synchronized audio for 75 participants performing linguistically motivated speech tasks, alongside the corresponding public domain raw RT-MRI data. The dataset also includes 3D volumetric vocal tract MRI during sustained speech sounds and high-resolution static anatomical T2-weighted upper airway MRI for each participant.
Funder
National Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
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