# Test-Time Training for Speech Enhancement

arXiv: https://arxiv.org/abs/2508.01847 · HTML: https://mlsanity.com/paper/2508.01847/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Dynamic Filter Networks](https://mlsanity.com/paper/1605.09673/) (2016)
- [Test-Time Training with Masked Autoencoders](https://mlsanity.com/paper/2209.07522/) (2022)
- [DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors](https://mlsanity.com/paper/2110.01763/) (2021)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation](https://mlsanity.com/paper/2302.05155/) (2023)
- [Importance Weighted Adversarial Nets for Partial Domain Adaptation](https://mlsanity.com/paper/1803.09210/) (2018)
- [WeightNet: Revisiting the Design Space of Weight Networks](https://mlsanity.com/paper/2007.11823/) (2020)
- [Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition](https://mlsanity.com/paper/2203.14222/) (2022)
- [Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation](https://mlsanity.com/paper/2105.03544/) (2021)
- [Domain Adaptation and Autoencoder Based Unsupervised Speech Enhancement](https://mlsanity.com/paper/2112.05036/) (2021)
- [Extending Audio Masked Autoencoders Toward Audio Restoration](https://mlsanity.com/paper/2305.06701/) (2023)
- [Test-Time Training for Speech](https://mlsanity.com/paper/2309.10930/) (2023)
- [Analysis of Noisy-target Training for DNN-based speech enhancement](https://mlsanity.com/paper/2211.01198/) (2022)
- [CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram](https://mlsanity.com/paper/2309.05975/) (2023)
- [Pre-training Feature Guided Diffusion Model for Speech Enhancement](https://mlsanity.com/paper/2406.07646/) (2024)

## Built on by (most cited first, 2 of 2)

- [Towards Lightweight Adaptation of Speech Enhancement Models in Real-World Environments](https://mlsanity.com/paper/2603.07471/) (2026)
- [Test-time adaptation for speech enhancement with an autoregressive speech prior](https://mlsanity.com/paper/2609.03622/) (2026)

## Abstract (arXiv)

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.