In this paper, we propose a novel deep transfer learning method called deep
implicit distribution alignment networks (DIDAN) to deal with cross-corpus
speech emotion recognition (SER) problem, in which the labeled training
(source) and unlabeled testing (target) speech signals come from different
corpora. Specifically, DIDAN first adopts a simple deep regression network
consisting of a set of convolutional and fully connected layers to directly
regress the source speech spectrums into the emotional labels such that the
proposed DIDAN can own the emotion discriminative ability. Then, such ability
is transferred to be also applicable to the target speech samples regardless of
corpus variance by resorting to a well-designed regularization term called
implicit distribution alignment (IDA). Unlike widely-used maximum mean
discrepancy (MMD) and its variants, the proposed IDA absorbs the idea of sample
reconstruction to implicitly align the distribution gap, which enables DIDAN to
learn both emotion discriminative and corpus invariant features from speech
spectrums. To evaluate the proposed DIDAN, extensive cross-corpus SER
experiments on widely-used speech emotion corpora are carried out. Experimental
results show that the proposed DIDAN can outperform lots of recent
state-of-the-art methods in coping with the cross-corpus SER tasks.

本文提出了一种深迁移学习方法 DIDAN 来解决来自不同语料库的标注训练语音信号和未标注测试语音信号的交叉语料库情感识别问题。DIDAN 通过将源语音谱直接回归到情感标签来拥有情感判别能力，并利用名为隐式分布对齐（IDA）的良好设计的正则化项将其转移为适用于目标语音样本，从而使 DIDAN 可以从语音谱学习到既具有情感鉴别又具有语料库不变性的特征。广泛的交叉语料库 SER 实验表明，DIDAN 可以在处理交叉语料库 SER 任务方面优于许多最新的最先进的方法。