UniSE: A Unified Framework for Decoder-Only Autoregressive LM-Based Speech Enhancement

Neural audio codecs have largely promoted the application of language models (LMs) for speech applications.

However, the effectiveness of autoregressive LM-based models in unifying speech enhancement (SE) tasks remains underexplored.

In this work, we propose UniSE, a unified decoder-only LM-based framework to handle different SE tasks including speech restoration, target speaker extraction, and speech separation.

Conditioned on input speech features, it autoregressively generates target discrete tokens, facilitating compatibility between distinct learning patterns of multiple tasks.

To further optimize speech quality, we introduce a progressive reinforcement learning strategy with multiple assessment criteria.

Experiments on several benchmarks show that UniSE achieves competitive performance compared to discriminative and generative baselines, demonstrating the capacity of LMs in unifying SE tasks.

The code and demo are available at: this https URL.