Summary of Transducers with Pronunciation-aware Embeddings For Automatic Speech Recognition, by Hainan Xu et al.
Transducers with Pronunciation-aware Embeddings for Automatic Speech Recognition
by Hainan Xu, Zhehuai Chen, Fei Jia, Boris Ginsburg
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 proposed Transducers with Pronunciation-aware Embeddings (PET) model enhances speech recognition accuracy in Mandarin Chinese and Korean datasets compared to conventional Transducers. By incorporating shared components for tokens with similar pronunciations, PET models consistently improve recognition rates. Additionally, the paper uncovers error chain reactions, where errors tend to group together in an utterance, but shows that PET models effectively mitigate this issue by reducing subsequent errors. The implementation will be open-sourced with the NeMo toolkit. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PET is a new model that helps machines recognize spoken words better. It uses special “embeddings” that connect similar sounds and words to help predict what was said. This makes speech recognition more accurate, especially for languages like Mandarin Chinese and Korean. The paper also found that mistakes tend to happen in patterns, but the PET model can break these chains by predicting what comes next correctly. |
