Summary of Multimodal Segmentation For Vocal Tract Modeling, by Rishi Jain et al.
Multimodal Segmentation for Vocal Tract Modeling
by Rishi Jain, Bohan Yu, Peter Wu, Tejas Prabhune, Gopala Anumanchipalli
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a deep labeling strategy for real-time magnetic resonance imaging (RT-MRI) videos to model the vocal tract. This is necessary for interpretable speech processing and linguistics, but challenging due to limited labeled datasets from occluded internal articulators. The authors introduce a multimodal algorithm using audio to improve segmentation of vocal articulators. They set a new benchmark in MRI video segmentation and release labels for a 75-speaker RT-MRI dataset, increasing the amount of labeled public data by over a factor of 9. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how we speak by creating more accurate models of our vocal tracts. This is important because it can improve how well computers recognize speech and help us learn more about languages. The authors developed a new way to label videos of the vocal tract taken with MRI, which is usually hard to do because many parts are hidden from view. They also created an algorithm that uses audio to help segment the different parts of the vocal tract. This has set a new standard for how well these videos can be labeled and will make it easier to create more accurate models. |
