Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks

Subin Erattakulangara; Karthika Kelat; Katie Burnham; Rachel Balbi; Sarah E Gerard; David Meyer; Sajan Goud Lingala

doi:10.48550/arxiv.2501.06229

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Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks

Preprint

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Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks

Subin Erattakulangara, Karthika Kelat, Katie Burnham, Rachel Balbi, Sarah E Gerard, David Meyer and Sajan Goud Lingala

arXiv.org

Cornell University

01/07/2025

DOI: 10.48550/arxiv.2501.06229

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Accurate segmentation of the vocal tract from magnetic resonance imaging (MRI) data is essential for various voice and speech applications. Manual segmentation is time intensive and susceptible to errors. This study aimed to evaluate the efficacy of deep learning algorithms for automatic vocal tract segmentation from 3D MRI.

Computer Science - Computer Vision and Pattern Recognition

Computer Science - Sound

Details

Title: Subtitle: Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks
Creators: Subin Erattakulangara
Karthika Kelat
Katie Burnham
Rachel Balbi
Sarah E Gerard
David Meyer
Sajan Goud Lingala
Resource Type: Preprint
Publication Details: arXiv.org
DOI: 10.48550/arxiv.2501.06229
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 01/07/2025
Academic Unit: School of Music; Roy J. Carver Department of Biomedical Engineering; Radiology; Communication Sciences and Disorders
Record Identifier: 9984773418702771

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