Multimodal 1. S. Rayatdoost, D. Radrauf, and M. Soleymani, "Multimodal Gated Information Fusion for Emotion Recognition from EEG Signals and Facial Behaviors". In Proceedings of the 22nd ACM International Conference on Multimodal Interaction, ICMI '20, New York, NY, USA. ACM, 2020.
Multimodal and Multi-view Models for Emotion Recognition By Gustavo Aguilar, Viktor Rozgic, Weiran Wang, Chao Wang. 2019. ... Optimize and port to different platforms audio and speech processing algorithms with focus on Voice Communication and Speech Recognition· Integrate vendor hardware and software stacks· Be able, and willing, to multi ...Emotion Recognition, Deep Learning, Multimodal Emotion Recognition, Adversarial Analysis, e-Healthcare Emotion Recognition System, Emotion User Experience, IoT Emotion Recognition Facial Emotion Recognition, Body Emotion Recognition, Speech Emotion Recognition. Guest Editors. Carmen Bisogni (Lead Guest Editor) University of Salerno, ItalyTime: 14:05-14:40, 15th October, 2021. Title: Multimodal Emotion Recognition. Abstract : Automatic emotion recognition is an indispensable ability of intelligent human-computer interaction systems.The behavior signals of human emotion expression are multimodal, including voice, facial expression, body language, bio-signals etc.
In this paper, a multimodal speech emotion recognition system has been developed, and a novel technique to explain its predictions has been proposed. The audio and textual features are extracted separately using attention-based Gated Recurrent Unit (GRU) and pre-trained Bidirectional Encoder Representations from Transformers (BERT), respectively.