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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
Masked Autoregressive Speech Enhancement with Continuous Neural Audio Codec Representations
TL;DR
Masked autoregressive speech enhancement (MARSE) leverages continuous neural audio codec representations to improve speech quality and intelligibility. This method allows for a flexible balance between performance and computational cost.
✦ Why It Matters
Experiment with MARSE to enhance your speech processing applications today.
Key Takeaways
- Explore continuous NAC representations for better speech enhancement.
- Utilize the Conformer model for consistent performance in SE tasks.
- Test different decoding policies to optimize computational costs.
- Access provided audio examples and code for practical implementation.
How It Works
MARSE operates by masking segments of clean speech and then using a continuous representation from the DAC codec to iteratively decode these segments. This approach allows the model to focus on reconstructing the missing parts of the audio, leading to enhanced clarity and intelligibility.