Summary of Language Complexity and Speech Recognition Accuracy: Orthographic Complexity Hurts, Phonological Complexity Doesn’t, by Chihiro Taguchi and David Chiang
Language Complexity and Speech Recognition Accuracy: Orthographic Complexity Hurts, Phonological Complexity Doesn’t
by Chihiro Taguchi, David Chiang
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates how linguistic factors affect the performance of Automatic Speech Recognition (ASR) models. It fine-tunes a multilingual self-supervised pretrained model, Wav2Vec2-XLSR-53, on 25 languages with 15 writing systems and examines the ASR accuracy, grapheme entropy, logographicity, and phonemes to determine how linguistic complexities impact performance. The results show that orthographic complexity is significantly correlated with low ASR accuracy, while phonological complexity has no significant correlation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers looked at how different language features affect a machine’s ability to recognize speech. They tested a special kind of AI model on many languages and writing systems. They wanted to see if the way words are written or how sounds are pronounced would make it harder for the AI to understand what people are saying. The study found that the way words are written makes a big difference, but how sounds are pronounced doesn’t. |
Keywords
* Artificial intelligence * Self supervised
