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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
Test-time adaptation for speech enhancement with an autoregressive speech prior
TL;DR
A novel test-time adaptation (TTA) method enhances speech quality by using an autoregressive prior trained on clean speech. This approach shows significant improvements in noisy conditions without needing labeled data.
✦ Why It Matters
Implement this TTA method to improve your speech enhancement models in diverse acoustic environments today.
Key Takeaways
- Utilize autoregressive priors for speech enhancement.
- Minimize Kullback-Leibler divergence for better adaptation.
- Test on various noisy datasets for robust performance.
- Access provided code and audio examples for implementation.
How It Works
The proposed TTA method leverages an autoregressive prior, which is a statistical model that predicts future speech samples based on past samples. By training this prior on clean speech representations, the model can adapt to noisy conditions by aligning the enhanced output with the clean speech distribution.
This is achieved through minimizing the Kullback-Leibler divergence, ensuring that the enhanced speech closely resembles the characteristics of clean speech.