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Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition

2022-01-17 09:46:59
Pengfei Liu, Kun Li, Helen Meng
arXiv_CL
                 
Abstract

Emotion recognition is a challenging and actively-studied research area that plays a critical role in emotion-aware human-computer interaction systems. In a multimodal setting, temporal alignment between different modalities has not been well investigated yet. This paper presents a new model named as Gated Bidirectional Alignment Network (GBAN), which consists of an attention-based bidirectional alignment network over LSTM hidden states to explicitly capture the alignment relationship between speech and text, and a novel group gated fusion (GGF) layer to integrate the representations of different modalities. We empirically show that the attention-aligned representations outperform the last-hidden-states of LSTM significantly, and the proposed GBAN model outperforms existing state-of-the-art multimodal approaches on the IEMOCAP dataset.

Abstract (translated)

URL

https://arxiv.org/abs/2201.06309

PDF

https://arxiv.org/pdf/2201.06309.pdf

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