Linked Source and Target Domain Subspace Feature Transfer Learning -- Exemplified by Speech Emotion Recognition
Jun Deng, Zixing Zhang, Björn Wolfgang Schuller · 2014
The typical inherent mismatch between the test and training corpora and by that between 'target' and 'source' sets usually leads to significant performance downgrades. To cope with this, this study presents a feature transfer learning method using Denoising Auto encoders (DAEs) to build high order subspaces of the source and target corpora, where features in the source domain are transferred to the target domain by an additional neural network. To exemplify effectiveness of our approach, we select the INTERSPEECH Emotion Challenge's FAU Aibo Emotion Corpus as target corpus and further two publicly available databases as source corpora for extensive and reproducible evaluation. The experimental results show that our method significantly improves over the baseline performance and outperforms today's state-of-the-art domain adaptation methods.