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

• URL: http://arxiv.org/abs/2201.06309v1
• Date: Mon, 17 Jan 2022 09:46:59 GMT
• Title: Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition
• Authors: Pengfei Liu, Kun Li and Helen Meng
• Abstract summary: 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. We empirically show that the attention-aligned representations outperform the last-hidden-states of LSTM significantly. The proposed GBAN model outperforms existing state-of-the-art multimodal approaches on the IEMOCAP dataset.
• Score: 63.07844685982738
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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