Summary of Coupling Speech Encoders with Downstream Text Models, by Ciprian Chelba and Johan Schalkwyk
Coupling Speech Encoders with Downstream Text Models
by Ciprian Chelba, Johan Schalkwyk
First submitted to arxiv on: 24 Jul 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 A novel modular approach to building cascade speech translation models is introduced, ensuring performance no worse than a 1-best baseline while maintaining state-of-the-art speech recognition and text translation capabilities. The key innovation lies in the use of an “exporter” layer trained under L2-loss to align ASR embeddings with MT token embeddings for the best sequence. This allows direct feeding of exporter output embeddings into the MT model, guaranteeing performance no worse than the baseline while enabling backpropagation gradients from the MT model to flow into ASR components. The matched-embeddings cascade architecture demonstrates a significant improvement over its 1-best counterpart in scenarios where incremental training is not feasible, leveraging data provided with the AST task. However, this gain disappears when the MT model is incrementally trained on parallel text data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make speech translation models better is introduced. This approach makes sure that the translated speech is at least as good as the best baseline model while keeping the best parts of previous models. The key idea is a special “exporter” layer that matches the words spoken with the words translated, ensuring that the two are aligned correctly. This allows the translator to learn from the speaker’s words and provide better translations. The approach works well when there isn’t enough data to train the translator further. |
Keywords
* Artificial intelligence * Backpropagation * Token * Translation
