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

What Happened

Researchers have introduced UniSE, a unified framework that leverages decoder-only autoregressive language models for speech enhancement tasks. The work, detailed in an updated arXiv preprint, addresses a gap in current audio AI research: while neural audio codecs have enabled language models to handle speech generation and recognition, the potential of autoregressive LMs for unifying multiple speech enhancement tasks—such as denoising, dereverberation, and bandwidth extension—has remained largely unexplored. UniSE proposes a single model architecture that can perform these varied enhancement functions without task-specific modifications, treating speech enhancement as a conditional generation problem within a language modeling paradigm.

Why It Matters

This development is significant for several reasons. First, it represents a move toward unification in audio processing. Traditionally, speech enhancement systems have been fragmented, with separate models for noise reduction, echo cancellation, and quality improvement. UniSE’s approach suggests that a single autoregressive LM, trained on codec representations of speech, can handle multiple enhancement objectives simultaneously—potentially reducing the engineering overhead of maintaining multiple specialized systems.

Second, the framework aligns with the broader industry trend of using decoder-only architectures, which have proven highly effective in text-based LMs. By applying this same paradigm to audio, UniSE could enable more seamless integration of speech enhancement into larger multimodal AI pipelines. For example, a single model could clean up noisy audio before feeding it into a speech recognition or translation system, eliminating the need for separate preprocessing modules.

Third, the research highlights the growing utility of neural audio codecs as a bridge between continuous audio signals and discrete token spaces that LMs can process. This approach could accelerate progress in other audio domains, such as music enhancement or environmental sound classification, by providing a template for how to adapt autoregressive LMs to non-text modalities.

Implications for AI Practitioners

For engineers building voice-enabled applications, UniSE offers a potential path to simpler, more maintainable stacks. Instead of deploying separate enhancement models for different noise profiles or acoustic conditions, practitioners could fine-tune a single unified model on their specific deployment data. This could reduce latency and memory footprint in edge devices, where running multiple models is often impractical.

However, practitioners should note that autoregressive models are inherently sequential—they generate outputs token by token, which can introduce latency compared to parallelized feed-forward architectures. For real-time applications like live voice calls, this trade-off between unification and speed must be carefully evaluated. Additionally, the reliance on neural audio codecs means that the quality of enhancement is bounded by the codec’s fidelity; practitioners should test whether codec artifacts are acceptable for their use case.

The research also underscores the importance of training data diversity. A unified model must generalize across many acoustic environments, which demands large, well-curated datasets. Teams with limited data may find that task-specific models still outperform a unified approach.

Key Takeaways

• UniSE demonstrates that a single decoder-only autoregressive LM can unify multiple speech enhancement tasks, reducing the need for separate specialized models.
• The framework leverages neural audio codecs to convert continuous audio into discrete tokens, enabling LM-based processing—a paradigm that could extend to other audio domains.
• AI practitioners should weigh the benefits of model unification against potential latency issues inherent in autoregressive generation, especially for real-time applications.
• Success with UniSE hinges on access to diverse, high-quality training data; teams with limited resources may still benefit from task-specific models.