Domain Adaptation without Catastrophic Forgetting on a Small-Scale Partially-Labeled Corpus for Speech Emotion Recognition

Zhi Zhu; Yoshinao Sato

ICASSP 2023accepted0 citations

Domain Adaptation without Catastrophic Forgetting on a Small-Scale Partially-Labeled Corpus for Speech Emotion Recognition

Abstract

Hence, developing a corpus for speech emotion recognition (SER) in the target domain is significant; however, this is time-consuming and cost-intensive. In this study, we aim to fully use a partially-labeled corpus in the target domain (target corpus) with the help of an existing fully-labeled corpus (common corpus). To this end, we proposed a method that leverages domain adversarial multi-task learning to reconcile the definitions of emotion classes across domains and noisy student training to utilize unlabeled data. Our experimental results demonstrated that the proposed method improved the SER performance in the target domain when the target corpus was small in size and imbalanced in classes. Furthermore, performance on the common corpus was not deteriorated by the proposed method.

BibTeX

@inproceedings{icassp2023_domainadaptation,
  title = {Domain Adaptation without Catastrophic Forgetting on a Small-Scale Partially-Labeled Corpus for Speech Emotion Recognition},
  author = {Zhi Zhu and Yoshinao Sato},
  booktitle = {ICASSP 2023},
  year = {2023}
}