Test-time adaptation for speech enhancement with an autoregressive speech prior

Abstract:Test-time adaptation (TTA) offers a promising direction for improving speech enhancement models under mismatched acoustic conditions, without requiring access to labeled target data. In this work, we propose a single-utterance TTA method that regularizes a pretrained speech enhancement model using an autoregressive prior trained on clean speech latent representations extracted from a neural audio codec. Adaptation is performed by minimizing the Kullback-Leibler divergence between the enhanced speech distribution and the clean speech prior. Experiments across multiple noisy speech datasets show consistent improvements in speech quality, particularly under training-testing noise mismatch conditions. Code and audio examples are available online.

Sound (cs.SD); Artificial Intelligence (cs.AI)

arXiv:2609.03622 [cs.SD]

(or arXiv:2609.03622v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.03622 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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