Multilevel Transformer For Multimodal Emotion Recognition

• URL: http://arxiv.org/abs/2211.07711v2
• Date: Wed, 16 Nov 2022 06:56:41 GMT
• Title: Multilevel Transformer For Multimodal Emotion Recognition
• Authors: Junyi He, Meimei Wu, Meng Li, Xiaobo Zhu, Feng Ye
• Abstract summary: We introduce a novel multi-granularity framework, which combines fine-grained representation with pre-trained utterance-level representation. Inspired by Transformer TTS, we propose a multilevel transformer model to perform fine-grained multimodal emotion recognition.
• Score: 6.0149102420697025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal emotion recognition has attracted much attention recently. Fusing multiple modalities effectively with limited labeled data is a challenging task. Considering the success of pre-trained model and fine-grained nature of emotion expression, it is reasonable to take these two aspects into consideration. Unlike previous methods that mainly focus on one aspect, we introduce a novel multi-granularity framework, which combines fine-grained representation with pre-trained utterance-level representation. Inspired by Transformer TTS, we propose a multilevel transformer model to perform fine-grained multimodal emotion recognition. Specifically, we explore different methods to incorporate phoneme-level embedding with word-level embedding. To perform multi-granularity learning, we simply combine multilevel transformer model with Albert. Extensive experimental results show that both our multilevel transformer model and multi-granularity model outperform previous state-of-the-art approaches on IEMOCAP dataset with text transcripts and speech signal.

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