Hybrid Multi-Task Learning for End-To-End Multimodal Emotion Recognition-Reference-Cited by-同舟云学术

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Hybrid Multi-Task Learning for End-To-End Multimodal Emotion Recognition

Published:2023-10-31 Issue: Volume: Page:
ISSN:
Container-title:2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
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Author:

Chen Junjie¹,Li Yongwei²,Zhao Ziping¹,Liu Xuefei²,Wen Zhengqi²,Tao Jianhua³

Affiliation:

1. Tianjin Normal University,College of Computer and Information Engineering,Tianjin,China

2. Chinese Academy of Sciences,National Laboratory of Pattern Recognition, Institute of Automation,Beijing,China

3. Tsinghua University,Department of Automation,Beijing,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10317071/10317095/10317160.pdf?arnumber=10317160

Reference26 articles.

1. A Multi-Scale Fusion Framework for Bimodal Speech Emotion Recognition

2. Multimodal emotion recognition using cross-modal attention and 1d convolutional neural networks;krishna;InterSpeech,2020

3. Improved End-to-End Speech Emotion Recognition Using Self Attention Mechanism and Multitask Learning

4. Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach

5. Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition

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