In this paper, we study the application of Test-Time Training (TTT) as a
solution to handling distribution shifts in speech applications. In particular,
we introduce distribution-shifts to the test datasets of standard
speech-classification tasks -- for example, speaker-identification and
emotion-detection -- and explore how Test-Time Training (TTT) can help adjust
to the distribution-shift. In our experiments that include distribution shifts
due to background noise and natural variations in speech such as gender and
age, we identify some key-challenges with TTT including sensitivity to
optimization hyperparameters (e.g., number of optimization steps and subset of
parameters chosen for TTT) and scalability (e.g., as each example gets its own
set of parameters, TTT is not scalable). Finally, we propose using BitFit -- a
parameter-efficient fine-tuning algorithm proposed for text applications that
only considers the bias parameters for fine-tuning -- as a solution to the
aforementioned challenges and demonstrate that it is consistently more stable
than fine-tuning all the parameters of the model.

本文研究了测试时间训练（TTT）在处理语音应用中的分布偏移问题的应用。我们引入分布偏移到标准语音分类任务的测试数据集中，并探索了测试时间训练（TTT）如何帮助调整到分布偏移。在我们的实验中，包括背景噪声和语音中的性别和年龄等自然变化引起的分布偏移，我们确定了 TTT 面临的一些关键挑战，包括对优化超参数（例如，优化步数和选择 TTT 的参数子集）的敏感性以及可扩展性（例如，由于每个示例都有自己的参数集，TTT 不具有可扩展性）。最后，我们提出使用 BitFit 作为解决上述挑战的参数高效微调算法，BitFit 仅考虑偏差参数进行微调，并证明它相对于微调模型的所有参数更加稳定。

测试时训练语音

Test-Time Training for Speech

We describe a general framework for online adaptation of optimization
hyperparameters by `hot swapping' their values during learning. We investigate
this approach in the context of adaptive learning rate selection using an
explore-exploit strategy from the multi-armed bandit literature. Experiments on
a benchmark neural network show that the hot swapping approach leads to
consistently better solutions compared to well-known alternatives such as
AdaDelta and stochastic gradient with exhaustive hyperparameter search.

本文描述了一种在线适应性优化超参数的一般框架，并研究了使用多臂老虎机探索 - 开发策略实现自适应学习率选择的方法。对基准神经网络的实验表明，与 AdaDelta 和利用彻底超参数搜索的随机梯度等众所周知的方案相比，热插拔方法能够稳定地获得更好的解决方案。