Test-Time Adaptation (TTA) is a critical paradigm for tackling distribution
shifts during inference, especially in visual recognition tasks. However, while
acoustic models face similar challenges due to distribution shifts in test-time
speech, TTA techniques specifically designed for acoustic modeling in the
context of open-world data shifts remain scarce. This gap is further
exacerbated when considering the unique characteristics of acoustic foundation
models: 1) they are primarily built on transformer architectures with layer
normalization and 2) they deal with test-time speech data of varying lengths in
a non-stationary manner. These aspects make the direct application of
vision-focused TTA methods, which are mostly reliant on batch normalization and
assume independent samples, infeasible. In this paper, we delve into TTA for
pre-trained acoustic models facing open-world data shifts. We find that noisy,
high-entropy speech frames, often non-silent, carry key semantic content.
Traditional TTA methods might inadvertently filter out this information using
potentially flawed heuristics. In response, we introduce a heuristic-free,
learning-based adaptation enriched by confidence enhancement. Noting that
speech signals' short-term consistency, we also apply consistency
regularization during test-time optimization. Our experiments on synthetic and
real-world datasets affirm our method's superiority over existing baselines.

本论文研究了面对开放世界数据变化的预训练声学模型的测试时间适应方法，通过引入无启发式、基于学习的自适应方法并结合置信度提升以及在测试时间优化中应用一致性规范化，证明了该方法在合成和真实数据集上的优越性。

提升开放世界转换中声学基础模型的测试时适应能力

Advancing Test-Time Adaptation for Acoustic Foundation Models in  Open-World Shifts

Test-Time Adaptation (TTA) allows to update pretrained models to changing
data distributions at deployment time. While early work tested these algorithms
for individual fixed distribution shifts, recent work proposed and applied
methods for continual adaptation over long timescales. To examine the reported
progress in the field, we propose the Continuously Changing Corruptions (CCC)
benchmark to measure asymptotic performance of TTA techniques. We find that
eventually all but one state-of-the-art methods collapse and perform worse than
a non-adapting model, including models specifically proposed to be robust to
performance collapse. In addition, we introduce a simple baseline, "RDumb",
that periodically resets the model to its pretrained state. RDumb performs
better or on par with the previously proposed state-of-the-art in all
considered benchmarks. Our results show that previous TTA approaches are
neither effective at regularizing adaptation to avoid collapse nor able to
outperform a simplistic resetting strategy.

TTA 技术在连续变化的扰动下的性能表现正在被热烈关注，我们提出了连续变化扰动基准（CCC）来测试 TTA 技术的渐近性能，并发现当前的 TTA 方法在避免崩溃和超越预训练模型方面的效果并不明显，实验表明简单的重置策略（RDumb）在所有考虑的基准测试中表现更好或不逊于之前提出的先进技术。