Summary of Back to Supervision: Boosting Word Boundary Detection Through Frame Classification, by Simone Carnemolla et al.
Back to Supervision: Boosting Word Boundary Detection through Frame Classification
by Simone Carnemolla, Salvatore Calcagno, Simone Palazzo, Daniela Giordano
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers propose a model-agnostic framework for supervised word boundary detection, which significantly improves upon existing methods. The framework employs labels augmentation techniques and output-frame selection strategies to achieve state-of-the-art performance on the Buckeye and TIMIT datasets. Specifically, when using the HuBERT encoder, the authors achieved F-values of 0.8427 and 0.7436, as well as R-values of 0.8489 and 0.7807, respectively, surpassing other architectures in both supervised and self-supervised settings. This approach not only sets a new state-of-the-art for these datasets but also offers a robust and efficient preprocessing method for future research in audio tokenization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way to identify word boundaries in speech, which is important for many speech processing tasks. The researchers created a framework that uses labels augmentation techniques and output-frame selection strategies to do this task well. They tested it on two datasets, Buckeye and TIMIT, and got better results than other methods. This new approach can be used as a starting point for future research in audio tokenization. |
Keywords
» Artificial intelligence » Encoder » Self supervised » Supervised » Tokenization
