UniPASE extends the low-hallucination PASE framework to universal speech enhancement, restoring seven distortion types at flexible sampling rates with better word-error and speaker-similarity scores than prior generative systems.
GenSE: Generative speech enhancement via language models using hierarchical modeling
2 Pith papers cite this work. Polarity classification is still indexing.
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SwitchCodec introduces Residual Experts Vector Quantization and a multi-tiered STFT discriminator to achieve PESQ 2.87 and ViSQOL 4.27 at 2.67 kbps while halving training time via post-training.
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UniPASE: A Generative Model for Universal Speech Enhancement with High Fidelity and Low Hallucinations
UniPASE extends the low-hallucination PASE framework to universal speech enhancement, restoring seven distortion types at flexible sampling rates with better word-error and speaker-similarity scores than prior generative systems.
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SwitchCodec: A High-Fidelity Nerual Audio Codec With Sparse Quantization
SwitchCodec introduces Residual Experts Vector Quantization and a multi-tiered STFT discriminator to achieve PESQ 2.87 and ViSQOL 4.27 at 2.67 kbps while halving training time via post-training.