Summary of Probing Self-attention in Self-supervised Speech Models For Cross-linguistic Differences, by Sai Gopinath and Joselyn Rodriguez
Probing self-attention in self-supervised speech models for cross-linguistic differences
by Sai Gopinath, Joselyn Rodriguez
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 explores the use of self-attention mechanisms in small self-supervised speech transformer models for automatic speech recognition (ASR) tasks. It examines whether these models learn language-independent speech representations and finds diverse attention patterns ranging from local to global, regardless of training language. The study highlights differences in attention patterns between Turkish and English and demonstrates that pretraining helps the model learn important phonological information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how small speech transformer models use self-attention mechanisms for ASR tasks. It wants to know if these models can understand speech without needing to know a specific language. The study finds that these models don’t just focus on one part of the speech, but instead look at different parts depending on the language they’re trained on. This helps them learn important information about sounds and words. |
Keywords
» Artificial intelligence » Attention » Pretraining » Self attention » Self supervised » Transformer
