{"title":"人类情感识别的多模型深度学习方法。","authors":"Lalitha Arumugam, Samydurai Arumugam, Pabitha Chidambaram, Kumaresan Govindasamy","doi":"10.1007/s11571-025-10304-3","DOIUrl":null,"url":null,"abstract":"<p><p>Emotion recognition is a difficult problem mainly because emotions are presented in different modalities including; speech, face, and text. In light of this, in this paper, we introduce a novel framework known as Audio, Visual, and Text Emotions Fusion Network that will enhance the approaches to analyzing emotions that can incorporate these dissimilar types of inputs efficiently for the enhancement of the existing approaches to analyzing emotions. Using specialized techniques, each modality in this framework shows Graph Attention Network-based Transformer Network by employing Graph Attention Networks to detect dependencies in facial regions; Hybrid Wav2Vec 2.0 and Convolutional Neural Network combines Wav2Vec 2.0, and Convolutional Neural Network to extract informative temporal and frequency domain audio features. Contextual and sequential text semantics are captured by Bidirectional Encoder Representations from Transformers with Bidirectional Gated Recurrent Unit. They are fused based on a novel attention-based mechanism that distributes weights depending on the emotional context and improves cross-modal interactions. Moreover, the Audio, Visual, and Text Emotions Fusion Network system effectively identifies emotions, and the result section that contains overall accuracy at 98.7%, precision at 98.2%, recall, at 97.2%, and F1-score of 97.49% makes the proposed approach strong and efficient for real-time emotion recognition strategies.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"123"},"PeriodicalIF":3.9000,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317966/pdf/","citationCount":"0","resultStr":"{\"title\":\"A multi-model deep learning approach for human emotion recognition.\",\"authors\":\"Lalitha Arumugam, Samydurai Arumugam, Pabitha Chidambaram, Kumaresan Govindasamy\",\"doi\":\"10.1007/s11571-025-10304-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Emotion recognition is a difficult problem mainly because emotions are presented in different modalities including; speech, face, and text. In light of this, in this paper, we introduce a novel framework known as Audio, Visual, and Text Emotions Fusion Network that will enhance the approaches to analyzing emotions that can incorporate these dissimilar types of inputs efficiently for the enhancement of the existing approaches to analyzing emotions. Using specialized techniques, each modality in this framework shows Graph Attention Network-based Transformer Network by employing Graph Attention Networks to detect dependencies in facial regions; Hybrid Wav2Vec 2.0 and Convolutional Neural Network combines Wav2Vec 2.0, and Convolutional Neural Network to extract informative temporal and frequency domain audio features. Contextual and sequential text semantics are captured by Bidirectional Encoder Representations from Transformers with Bidirectional Gated Recurrent Unit. They are fused based on a novel attention-based mechanism that distributes weights depending on the emotional context and improves cross-modal interactions. Moreover, the Audio, Visual, and Text Emotions Fusion Network system effectively identifies emotions, and the result section that contains overall accuracy at 98.7%, precision at 98.2%, recall, at 97.2%, and F1-score of 97.49% makes the proposed approach strong and efficient for real-time emotion recognition strategies.</p>\",\"PeriodicalId\":10500,\"journal\":{\"name\":\"Cognitive Neurodynamics\",\"volume\":\"19 1\",\"pages\":\"123\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317966/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Neurodynamics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s11571-025-10304-3\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/8/2 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Neurodynamics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11571-025-10304-3","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/8/2 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
A multi-model deep learning approach for human emotion recognition.
Emotion recognition is a difficult problem mainly because emotions are presented in different modalities including; speech, face, and text. In light of this, in this paper, we introduce a novel framework known as Audio, Visual, and Text Emotions Fusion Network that will enhance the approaches to analyzing emotions that can incorporate these dissimilar types of inputs efficiently for the enhancement of the existing approaches to analyzing emotions. Using specialized techniques, each modality in this framework shows Graph Attention Network-based Transformer Network by employing Graph Attention Networks to detect dependencies in facial regions; Hybrid Wav2Vec 2.0 and Convolutional Neural Network combines Wav2Vec 2.0, and Convolutional Neural Network to extract informative temporal and frequency domain audio features. Contextual and sequential text semantics are captured by Bidirectional Encoder Representations from Transformers with Bidirectional Gated Recurrent Unit. They are fused based on a novel attention-based mechanism that distributes weights depending on the emotional context and improves cross-modal interactions. Moreover, the Audio, Visual, and Text Emotions Fusion Network system effectively identifies emotions, and the result section that contains overall accuracy at 98.7%, precision at 98.2%, recall, at 97.2%, and F1-score of 97.49% makes the proposed approach strong and efficient for real-time emotion recognition strategies.
期刊介绍:
Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models.
The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome.
The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged.
1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics.
2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages.
3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.