Unsupervised domain adaptation for speech emotion recognition using PCANet

Research in emotion recognition seeks to develop insights into the variances of features of emotion in one common domain. However, automatic emotion recognition from speech is challenging when training data and test data are drawn from different domains due to different recording conditions, languages, speakers and many other factors. In this paper, we propose a novel feature transfer approach with PCANet (a deep network), which extracts both the domain-shared and the domain-specific latent features to facilitate performance improvement. The proposal attempts to learn multiple intermediate feature representations along an interpolating path between the source and target domains using PCANet by considering the distribution shift between source domain and target domain, and then aligns other feature representations on the path with target subspace to control them to change in the right direction towards the target. To exemplify the effectiveness of our approach, we select the INTERSPEECH 2009 Emotion Challenge’s FAU Aibo Emotion Corpus as the target database and two public databases (ABC and Emo-DB) as source set. Experimental results demonstrate that the proposed feature transfer learning method outperforms the conventional machine learning methods and other transfer learning methods on the performance.