Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation

TRANSFERABLE POSITIVE/NEGATIVE SPEECH EMOTION RECOGNITION VIA CLASS-WISE ADVERSARIAL DOMAIN ADAPTATION Hao Zhou, Ke Chen School of Computer Science, The University of Manchester, Manchester, M13 9PL, U.K. ABSTRACT Speech emotion recognition plays an important role in building more intelligent and humanlike agents. Due to the difficulty of collecting speech emotional data, an increasingly popular solution is leveraging a related and rich source corpus to help address the target corpus. However, domain shift between the corpora poses a serious challenge, making domain shift adaptation difficult to function even on the recognition of positive/negative emotions. In this work, we propose class-wise adversarial domain adaptation to address this challenge by reducing the shift for all classes between different corpora. Experiments on the well-known corpora EMODB and Aibo demonstrate that our method is effective even when only a very limited number of target labeled examples are provided.