Abstract
In this work, we propose a novel variational Bayesian adaptive learning approach for cross-domain knowledge transfer to address acoustic mismatches between training and testing conditions, such as recording devices and environmental noise. Different from the traditional Bayesian approaches that impose uncertainties on model parameters risking the curse of dimensionality due to the huge number of parameters, we focus on estimating a manageable number of latent variables in deep neural models. Knowledge learned from a source domain is thus encoded in prior distributions of deep latent variables and optimally combined, in a Bayesian sense, with a small set of adaptation data from a target domain to approximate the corresponding posterior distributions. Two different strategies are proposed and investigated to estimate the posterior distributions: Gaussian mean-field variational inference, and empirical Bayes. These strategies address the presence or absence of parallel data in the source and target domains. Furthermore, structural relationship modeling is investigated to enhance the approximation. We evaluated our proposed approaches on two acoustic adaptation tasks: 1) device adaptation for acoustic scene classification, and 2) noise adaptation for spoken command recognition. Experimental results show that the proposed variational Bayesian adaptive learning approach can obtain good improvements on target domain data, and consistently outperforms state-of-the-art knowledge transfer methods.
Abstract (translated)
在这项工作中，我们提出了一种新颖的变分贝叶斯自适应学习方法，用于跨域知识迁移，以解决训练和测试条件之间声学不匹配的问题，如录音设备和环境噪声。与传统的贝叶斯方法不同，后者通过在模型参数上施加不确定性来应对维度灾难问题（由于大量的参数数量），我们专注于估计深层神经网络中有限数量的潜在变量。从源域学到的知识因此被编码为深度潜在变量的先验分布，并在贝叶斯意义上与目标领域的少量适应数据集相结合，以近似相应的后验分布。为了估算这些后验分布，提出了并研究了两种不同的策略：高斯均值场变分推理和经验贝叶斯方法。这两种策略分别处理源域和目标域中是否存在平行数据的问题。此外，还探讨了结构关系建模以增强近似效果。 我们在两项声学适应任务上评估了我们提出的方法：1）用于声景分类的设备自适应；2）用于语音命令识别的噪声自适应。实验结果表明，所提出的变分贝叶斯自适应学习方法在目标领域数据上可以获得显著改进，并且始终优于当前最先进的知识迁移方法。
URL
https://arxiv.org/abs/2501.15496
