Summary of Self-supervised Speech Representations Are More Phonetic Than Semantic, by Kwanghee Choi et al.
Self-Supervised Speech Representations are More Phonetic than Semantic
by Kwanghee Choi, Ankita Pasad, Tomohiko Nakamura, Satoru Fukayama, Karen Livescu, Shinji Watanabe
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); 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 Self-supervised speech models (S3Ms) have become a popular choice for speech applications, encoding linguistic properties in their word representations. This study delves deeper into the linguistic properties encoded by S3Ms at the word level, curating a dataset of near homophone and synonym word pairs to measure similarities between S3M word representation pairs. The results show that S3M representations consistently exhibit more phonetic than semantic similarity. Furthermore, this work questions whether widely used intent classification datasets, such as Fluent Speech Commands and Snips Smartlights, are adequate for measuring semantic abilities. A simple baseline using only the word identity surpasses S3M-based models, suggesting that high scores on these datasets do not necessarily guarantee the presence of semantic content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how speech models work with words. It finds out what features they pick up from similar-sounding and meaning words. The results show that these models are better at recognizing sounds than meanings. This is important because some datasets used to measure language skills might not be good measures after all. A simple way of doing things actually works better than using the fancy speech models. |
Keywords
» Artificial intelligence » Classification » Self supervised
