Improving Code-Switching ASR with Code-Mixing Guided Synthetic Speech
📰 ArXiv cs.AI
Improve code-switching ASR with synthetic speech generated using code-mixing guidance, enhancing language-boundary consistency and ASR accuracy
Action Steps
- Build a code-mixing guided TTS model to generate synthetic speech
- Configure the model to prioritize language-boundary consistency
- Apply the synthetic speech to augment ASR training data
- Test the ASR system with the augmented data
- Evaluate the performance of the ASR system using metrics such as word error rate
Who Needs to Know This
Researchers and developers working on ASR systems can benefit from this approach to improve code-switching ASR accuracy, while data scientists and engineers can apply this method to generate high-quality synthetic data for training
Key Insight
💡 Code-mixing guidance in TTS can enhance language-boundary consistency, leading to better ASR accuracy
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🗣️ Improve code-switching ASR with code-mixing guided synthetic speech! 🚀
Key Takeaways
Improve code-switching ASR with synthetic speech generated using code-mixing guidance, enhancing language-boundary consistency and ASR accuracy
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