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Multi-modal Attention for Speech Emotion Recognition

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arxiv 2009.04107 v1 pith:43QWM66N submitted 2020-09-09 eess.AS cs.MMcs.SDeess.IV

Zexu Pan , Zhaojie Luo , Jichen Yang , Haizhou Li This is my paper · ORCID
classification eess.AScs.MMcs.SDeess.IV
keywords speechemotionattentionrecognitionclstm-mmacuesfusionmulti-modal
verification ladder T0 review T1 audit T2 compute T3 formal
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Emotion represents an essential aspect of human speech that is manifested in speech prosody. Speech, visual, and textual cues are complementary in human communication. In this paper, we study a hybrid fusion method, referred to as multi-modal attention network (MMAN) to make use of visual and textual cues in speech emotion recognition. We propose a novel multi-modal attention mechanism, cLSTM-MMA, which facilitates the attention across three modalities and selectively fuse the information. cLSTM-MMA is fused with other uni-modal sub-networks in the late fusion. The experiments show that speech emotion recognition benefits significantly from visual and textual cues, and the proposed cLSTM-MMA alone is as competitive as other fusion methods in terms of accuracy, but with a much more compact network structure. The proposed hybrid network MMAN achieves state-of-the-art performance on IEMOCAP database for emotion recognition.

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