MAEC: Multi-Instance Learning with an Adversarial Auto-Encoder-Based Classifier for Speech Emotion Recognition

Changzeng Fu; Chaoran Liu; Carlos Toshinori Ishi; Hiroshi Ishiguro

ICASSP 2021accepted0 citations

MAEC: Multi-Instance Learning with an Adversarial Auto-Encoder-Based Classifier for Speech Emotion Recognition

Changzeng Fu, Chaoran Liu, Carlos Toshinori Ishi, Hiroshi Ishiguro

Abstract

In this paper, we propose an adversarial auto-encoder-based classifier, which can regularize the distribution of latent representation to smooth the boundaries among categories. Moreover, we adopt multi-instance learning by dividing speech into a bag of segments to capture the most salient moments for presenting an emotion. The proposed model was trained on the IEMOCAP dataset and evaluated on the in-corpus validation set (IEMOCAP) and the cross-corpus validation set (MELD). The experiment results show that our model outperforms the baseline on in-corpus validation and increases the scores on cross-corpus validation with regularization.

BibTeX

@inproceedings{icassp2021_maecmultiinstanc,
  title = {MAEC: Multi-Instance Learning with an Adversarial Auto-Encoder-Based Classifier for Speech Emotion Recognition},
  author = {Changzeng Fu and Chaoran Liu and Carlos Toshinori Ishi and Hiroshi Ishiguro},
  booktitle = {ICASSP 2021},
  year = {2021}
}