Test-Time Training for Speech Enhancement

• URL: http://arxiv.org/abs/2508.01847v1
• Date: Sun, 03 Aug 2025 17:02:55 GMT
• Title: Test-Time Training for Speech Enhancement
• Authors: Avishkar Behera, Riya Ann Easow, Venkatesh Parvathala, K. Sri Rama Murty,
• Abstract summary: This paper introduces a novel application of Test-Time Training (TTT) for Speech Enhancement.<n>It addresses the challenges posed by unpredictable noise conditions and domain shifts.<n>We show consistent improvements across speech quality metrics, outperforming the baseline model.
• Score: 2.9598903898834497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a novel application of Test-Time Training (TTT) for Speech Enhancement, addressing the challenges posed by unpredictable noise conditions and domain shifts. This method combines a main speech enhancement task with a self-supervised auxiliary task in a Y-shaped architecture. The model dynamically adapts to new domains during inference time by optimizing the proposed self-supervised tasks like noise-augmented signal reconstruction or masked spectrogram prediction, bypassing the need for labeled data. We further introduce various TTT strategies offering a trade-off between adaptation and efficiency. Evaluations across synthetic and real-world datasets show consistent improvements across speech quality metrics, outperforming the baseline model. This work highlights the effectiveness of TTT in speech enhancement, providing insights for future research in adaptive and robust speech processing.

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