Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917939899269120 |
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| author | Erattakulangara, Subin Kelat, Karthika Burnham, Katie Balbi, Rachel Gerard, Sarah E. Meyer, David Lingala, Sajan Goud |
| author_facet | Erattakulangara, Subin Kelat, Karthika Burnham, Katie Balbi, Rachel Gerard, Sarah E. Meyer, David Lingala, Sajan Goud |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06229 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks Erattakulangara, Subin Kelat, Karthika Burnham, Katie Balbi, Rachel Gerard, Sarah E. Meyer, David Lingala, Sajan Goud Computer Vision and Pattern Recognition Sound Audio and Speech Processing 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. |
| title | Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks |
| topic | Computer Vision and Pattern Recognition Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2501.06229 |