Multi-Task Momentum Distillation for Multimodal Sentiment Analysis

Lin, Ronghao; Hu, Haifeng

doi:10.1109/taffc.2023.3282410

Cited by 6 publications

(1 citation statement)

References 56 publications

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“…At the same time, multimodal sentiment analysis [ 46 ] is also very popular. It has richer emotional information and is more comprehensive in predicting and identifying human emotions.…”

Section: Discussionmentioning

confidence: 99%

A New Network Structure for Speech Emotion Recognition Research

Xu,

Liu,

Song

et al. 2024

Sensors

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Deep learning promotes the breakthrough of emotion recognition in many fields, especially speech emotion recognition (SER). As an important part of speech emotion recognition, the most relevant acoustic feature extraction has always attracted the attention of existing researchers. Aiming at the problem that the emotional information contained in the current speech signals is distributed dispersedly and cannot comprehensively integrate local and global information, this paper presents a network model based on a gated recurrent unit (GRU) and multi-head attention. We evaluate our proposed emotion model on the IEMOCAP and Emo-DB corpora. The experimental results show that the network model based on Bi-GRU and multi-head attention is significantly better than the traditional network model at detecting multiple evaluation indicators. At the same time, we also apply the model to a speech sentiment analysis task. On the CH-SIMS and MOSI datasets, the model shows excellent generalization performance.

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“…At the same time, multimodal sentiment analysis [ 46 ] is also very popular. It has richer emotional information and is more comprehensive in predicting and identifying human emotions.…”

Section: Discussionmentioning

confidence: 99%