Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19335
Title: A Self-Attentive Emotion Recognition Network
Authors: Partaourides, Harris 
Papadamou, Kostantinos 
Kourtellis, Nicolas 
Leontiades, Ilias 
Chatzis, Sotirios P. 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Deep Learning;Emotion Recognition;Self-Attention
Issue Date: 14-May-2020
Source: IEEE International Conference on Acoustics, Speech and Signal Processing, 4-8 May 2020, Barcelona, Spain
Conference: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 
Abstract: Attention networks constitute the state-of-the-art paradigm for capturing long temporal dynamics. This paper examines the efficacy of this paradigm in the challenging task of emotion recognition in dyadic conversations. In this work, we introduce a novel attention mechanism capable of inferring the immensity of the effect of each past utterance on the current speaker emotional state. The proposed self-attention network captures the correlation patterns among consecutive encoder network states, thus enabling the robust and effective modeling of temporal dynamics over arbitrary long temporal horizons. We exhibit the effectiveness of our approach considering the challenging IEMOCAP benchmark. We show that, our devised methodology outperforms state-of-the-art alternatives and commonly used approaches, giving rise to promising new research directions in the context of Online Social Network (OSN) analysis tasks.
URI: https://hdl.handle.net/20.500.14279/19335
ISBN: 978-1-5090-6631-5
DOI: 10.1109/ICASSP40776.2020.9054762
Rights: © IEEE.
Type: Conference Papers
Affiliation : Cyprus University of Technology 
Telefonica Research 
Samsung 
Publication Type: Peer Reviewed
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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