Using the model's own encoder as a feature loss improves perceptual quality and iterative stability of a speech enhancement model compared with a WavLM-based loss.
It has an encoder that extracts relevant fea- tures and passes them into a first-stage decoder
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Model as Loss: A Self-Consistent Training Paradigm
Using the model's own encoder as a feature loss improves perceptual quality and iterative stability of a speech enhancement model compared with a WavLM-based loss.