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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
Traceable TTS: Toward Watermark-Free TTS with Strong Traceability
TL;DR
A novel framework for Text-To-Speech (TTS) technology enhances traceability without using watermarks, improving audio quality. This approach addresses security concerns while maintaining high fidelity in synthetic speech.
✦ Why It Matters
Implement this watermark-free TTS framework to enhance security in your audio applications without sacrificing quality.
Key Takeaways
- Explore joint training methods for TTS models to improve traceability.
- Avoid traditional watermarking techniques that degrade audio quality.
- Focus on enhancing security in synthetic speech applications.
- Stay updated on the release of the code for practical implementation.
How It Works
The proposed framework employs a joint training method where both the TTS model and a discriminator are trained together. This allows the model to learn to generate speech that can be traced back to its source without embedding watermarks, thus maintaining high audio quality.