Speaker-Invariant Adversarial Domain Adaptation for Emotion Recognition

Automatic emotion recognition methods are sensitive to the variations across different datasets and their performance drops when evaluated across corpora. We can apply domain adaptation techniques e.g., Domain-Adversarial Neural Network (DANN) to mitigate this problem. Though the DANN can detect and remove the bias between corpora, the bias between speakers still remains which results in reduced performance. In this paper, we propose Speaker-Invariant Domain-Adversarial Neural Network (SIDANN) to reduce both the domain bias and the speaker bias. Specifically, based on the DANN, we add a speaker discriminator to unlearn information representing speakers' individual characteristics with a gradient reversal layer (GRL). Our experiments with multimodal data (speech, vision, and text) and the cross-domain evaluation indicate that the proposed SIDANN outperforms (+5.6% and +2.8% on average for detecting arousal and valence) the DANN model, suggesting that the SIDANN has a better domain adaptation ability than the DANN. Besides, the modality contribution analysis shows that the acoustic features are the most informative for arousal detection while the lexical features perform the best for valence detection.