Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better

• URL: http://arxiv.org/abs/2409.18971v1
• Date: Thu, 12 Sep 2024 05:05:34 GMT
• Title: Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better
• Authors: Mengying Ge, Mingyang Li, Dongkai Tang, Pengbo Li, Kuo Liu, Shuhao Deng, Songbai Pu, Long Liu, Yang Song, Tao Zhang,
• Abstract summary: We present our solutions for emotion recognition in the sub-challenges of Multimodal Emotion Recognition Challenge (MER2024) To mitigate the modal competition issue between audio and text, we adopt an early fusion strategy. Our model ranks textbf2nd in both MER2024-SEMI and MER2024-NOISE, validating our method's effectiveness.
• Score: 9.378013909890374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present our solutions for emotion recognition in the sub-challenges of Multimodal Emotion Recognition Challenge (MER2024). To mitigate the modal competition issue between audio and text, we adopt an early fusion strategy based on a large language model, where joint training of audio and text is conducted initially. And the joint Audio-Text modal feature will be late-fused with other unimodal features. In order to solve the problems of data insufficiency and class imbalance, We use multiple turns of multi-model voting for data mining. Moreover, to enhance the quality of audio features, we employ speech source separation to preprocess audios. Our model ranks \textbf{2nd} in both MER2024-SEMI and MER2024-NOISE, validating our method's effectiveness.

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