ArticleSensors (Basel, Switzerland)2024
A New Network Structure for Speech Emotion Recognition Research.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Research on contextual sentiment recognition based on neural encoding and decoding and knowledge guidance.Scientific reports · 2026Article
- MAGTF-Net: Dynamic Speech Emotion Recognition with Multi-Scale Graph Attention and LLD Feature Fusion.Sensors (Basel, Switzerland) · 2025Article
- Fusion of PCA and ICA in Statistical Subset Analysis for Speech Emotion Recognition.Sensors (Basel, Switzerland) · 2024Article
- Advancements in Sensors and Analyses for Emotion Sensing.Sensors (Basel, Switzerland) · 2024Article
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5 authors.
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Abstract
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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