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.
WavLM: Large-scale self-supervised pre- training for full stack speech processing
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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.