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arXiv · 2501.06229

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
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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.

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Subin Erattakulangara, Karthika Kelat, Katie Burnham, Rachel Balbi, Sarah E. Gerard, David Meyer, Sajan Goud Lingala. 2025-01-08. Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks. https://arxiv.org/abs/2501.06229

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