WGAN Domain Adaptation for EEG-Based Emotion Recognition

2018 ◽  
pp. 275-286 ◽  
Author(s):  
Yun Luo ◽  
Si-Yang Zhang ◽  
Wei-Long Zheng ◽  
Bao-Liang Lu
Keyword(s):  
Emotion Recognition ◽  
Domain Adaptation

scholarly journals Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1579 ◽  
Author(s):  
Kyoung Ju Noh ◽  
Chi Yoon Jeong ◽  
Jiyoun Lim ◽  
Seungeun Chung ◽  
Gague Kim ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Short Term Memory ◽  
Domain Adaptation ◽  
Classification Model ◽  
Speech Emotion Recognition ◽  
Target Domain ◽  
Model Generalization ◽  
Speech Database ◽  
Emotion Labels ◽  
Temporal Feature

Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization.

scholarly journals Universum Autoencoder-Based Domain Adaptation for Speech Emotion Recognition

2017 ◽  
Vol 24 (4) ◽  
pp. 500-504 ◽  
Author(s):  
Jun Deng ◽  
Xinzhou Xu ◽  
Zixing Zhang ◽  
Sascha Fruhholz ◽  
Bjorn Schuller
Keyword(s):  
Emotion Recognition ◽  
Domain Adaptation ◽  
Speech Emotion Recognition

scholarly journals Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition

2021 ◽  
Author(s):  
Haoqi Li ◽  
Yelin Kim ◽  
Cheng-Hao Kuo ◽  
Shrikanth S. Narayanan
Keyword(s):  
Emotion Recognition ◽  
Domain Adaptation

Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity

2020 ◽  
Vol 12 (2) ◽  
pp. 344-353 ◽  
Author(s):  
Jinpeng Li ◽  
Shuang Qiu ◽  
Changde Du ◽  
Yixin Wang ◽  
Huiguang He
Keyword(s):  
Emotion Recognition ◽  
Domain Adaptation
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