IEEE Transactions on Affective Computing最新文献_第10页

A New Look At Breathing for Affective Studies 情感研究中的呼吸新视角

IF 11.2 2区计算机科学

IEEE Transactions on Affective Computing Pub Date : 2024-06-12 DOI: 10.1109/taffc.2024.3413053

Nanfei Sun, Ioannis Pavlidis

引用次数: 0

Identifying Children with Autism Spectrum Disorder via Transformer-Based Representation Learning from Dynamic Facial Cues 通过基于变压器的动态面部线索表征学习识别自闭症谱系障碍儿童

IF 11.2 2区计算机科学

IEEE Transactions on Affective Computing Pub Date : 2024-06-11 DOI: 10.1109/taffc.2024.3412032

Chen Xia, Hexu Chen, Junwei Han, Dingwen Zhang, Kuan Li

引用次数: 0

Controllable Multi-Speaker Emotional Speech Synthesis With Emotion Representation of High Generalization Capability 具有高泛化能力情感表征的可控多扬声器情感语音合成

IF 11.2 2区计算机科学

IEEE Transactions on Affective Computing Pub Date : 2024-06-11 DOI: 10.1109/taffc.2024.3412152

Junjie Zheng, Jian Zhou, Wenming Zheng, Liang Tao, Hon Keung Kwan

引用次数: 0

Minority Views Matter: Evaluating Speech Emotion Classifiers with Human Subjective Annotations by an All-Inclusive Aggregation Rule 少数意见很重要：用全包式聚合规则评估带有人类主观注释的语音情感分类器

IF 11.2 2区计算机科学

IEEE Transactions on Affective Computing Pub Date : 2024-06-07 DOI: 10.1109/taffc.2024.3411290

Huang-Cheng Chou, Lucas Goncalves, Seong-Gyun Leem, Ali N. Salman, Chi-Chun Lee, Carlos Busso

引用次数: 0

Towards Generalised and Incremental Bias Mitigation in Personality Computing 在人格计算中实现通用和渐进式偏差缓解

IF 9.6 2区计算机科学

IEEE Transactions on Affective Computing Pub Date : 2024-06-05 DOI: 10.1109/TAFFC.2024.3409830

Jian Jiang;Viswonathan Manoranjan;Hanan Salam;Oya Celiktutan

{"title":"Towards Generalised and Incremental Bias Mitigation in Personality Computing","authors":"Jian Jiang;Viswonathan Manoranjan;Hanan Salam;Oya Celiktutan","doi":"10.1109/TAFFC.2024.3409830","DOIUrl":"10.1109/TAFFC.2024.3409830","url":null,"abstract":"Building systems for predicting human socio-emotional states has promising applications; however, if trained on biased data, such systems could inadvertently yield biased decisions. Bias mitigation remains an open problem, which tackles the correction of a model's disparate performance over different groups defined by particular sensitive attributes (e.g., gender, age, and race). In this work, we design a novel fairness loss function named Multi-Group Parity (MGP) to provide a generalised approach for bias mitigation in personality computing. In contrast to existing works in the literature, MGP is generalised as it features four ‘multiple’ properties (4Mul): multiple tasks, multiple modalities, multiple sensitive attributes, and multi-valued attributes. Moreover, we explore how to incrementally mitigate the biases when more sensitive attributes are taken into consideration sequentially. Towards this problem, we introduce a novel algorithm that utilises an incremental learning framework to mitigate bias against one attribute data at a time without compromising past fairness. Extensive experiments on two large-scale multi-modal personality recognition datasets validate the effectiveness of our approach in achieving superior bias mitigation under the proposed four properties and incremental debiasing settings.","PeriodicalId":13131,"journal":{"name":"IEEE Transactions on Affective Computing","volume":"15 4","pages":"2192-2203"},"PeriodicalIF":9.6,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

U-Shaped Distribution Guided Sign Language Emotion Recognition With Semantic and Movement Features 利用语义和动作特征的 U 型分布引导手语情绪识别

IF 9.6 2区计算机科学

IEEE Transactions on Affective Computing Pub Date : 2024-06-04 DOI: 10.1109/TAFFC.2024.3409357

Jiangtao Zhang;Qingshan Wang;Qi Wang

{"title":"U-Shaped Distribution Guided Sign Language Emotion Recognition With Semantic and Movement Features","authors":"Jiangtao Zhang;Qingshan Wang;Qi Wang","doi":"10.1109/TAFFC.2024.3409357","DOIUrl":"10.1109/TAFFC.2024.3409357","url":null,"abstract":"Emotional expression is a bridge to human communication, especially for the hearing impaired. This paper proposes a sign language emotion recognition method based on semantic and movement features by exploring the relationship between emotion valence and arousal in-depth, called SeMER. The SeMER framework includes a semantic extractor, a movement feature extractor, and an emotion classifier. The contextual relations obtained from the sign language recognition task are added to the semantic extractor as prior knowledge using a transfer learning approach to better acquire the affective polarity of semantics. In the movement feature extractor based on graph convolutional networks, a spatial-temporal adjacency matrix of gestures and node attention matrix are developed to aggregate the emotion-related movement features of intra- and inter-gestures. The proposed emotion classifier maps semantic and movement features to the emotion space. The validated U-shaped distributions of valance and arousal are then used to guide the relationship between them, and improve the accuracy of emotion prediction. In addition, a sign language emotion dataset containing 5 emotions from 18 participants, SE-Sentence, is collected through armbands with built-in surface electromyograph and inertial measurement unit sensors. Experimental results showed that SeMER achieved an accuracy and f1 value of 88% on SE-Sentence.","PeriodicalId":13131,"journal":{"name":"IEEE Transactions on Affective Computing","volume":"15 4","pages":"2180-2191"},"PeriodicalIF":9.6,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FEAD: Introduction to the fNIRS-EEG Affective Database - Video Stimuli FEAD： fNIRS-EEG 情感数据库简介 - 视频刺激

IF 11.2 2区计算机科学

IEEE Transactions on Affective Computing Pub Date : 2024-05-30 DOI: 10.1109/taffc.2024.3407380

Alireza Farrokhi Nia, Vanessa Tang, Valery Malyshau, Amit Barde, Gonzalo Maso Talou, Mark Billinghurst

引用次数: 0

Emotion Recognition in the Real-World: Passively Collecting and Estimating Emotions from Natural Speech Data of Individuals with Bipolar Disorder 真实世界中的情绪识别：从双相情感障碍患者的自然语音数据中被动收集和估计情感

IF 11.2 2区计算机科学

IEEE Transactions on Affective Computing Pub Date : 2024-05-30 DOI: 10.1109/taffc.2024.3407683

Emily Mower Provost, Sarah H Sperry, James Tavernor, Steve Anderau, Anastasia Yocum, Melvin G McInnis

引用次数: 0

Dynamic Causal Disentanglement Model for Dialogue Emotion Detection 用于对话情感检测的动态因果解缠模型

IF 11.2 2区计算机科学

IEEE Transactions on Affective Computing Pub Date : 2024-05-29 DOI: 10.1109/taffc.2024.3406710

Yuting Su, Yichen Wei, Weizhi Nie, Sicheng Zhao, Anan Liu

引用次数: 0

Bridge Graph Attention Based Graph Convolution Network With Multi-Scale Transformer for EEG Emotion Recognition 基于多尺度变换器的桥图注意图卷积网络用于脑电图情感识别

IF 9.6 2区计算机科学

IEEE Transactions on Affective Computing Pub Date : 2024-04-30 DOI: 10.1109/TAFFC.2024.3394873

Huachao Yan;Kailing Guo;Xiaofen Xing;Xiangmin Xu

{"title":"Bridge Graph Attention Based Graph Convolution Network With Multi-Scale Transformer for EEG Emotion Recognition","authors":"Huachao Yan;Kailing Guo;Xiaofen Xing;Xiangmin Xu","doi":"10.1109/TAFFC.2024.3394873","DOIUrl":"10.1109/TAFFC.2024.3394873","url":null,"abstract":"In multichannel electroencephalograph (EEG) emotion recognition, most graph-based studies employ shallow graph model for spatial characteristics learning due to node over-smoothing caused by an increase in network depth. To address over-smoothing, we propose the bridge graph attention-based graph convolution network (BGAGCN). It bridges previous graph convolution layers to attention coefficients of the final layer by adaptively combining each graph convolution output based on the graph attention network, thereby enhancing feature distinctiveness. Considering that graph-based network primarily focus on local EEG channel relationships, we introduce a transformer for global dependency. Inspired by the neuroscience finding that neural activities of different timescales reflect distinct spatial connectivities, we modify the transformer to a multi-scale transformer (MT) by applying multi-head attention to multichannel EEG signals after 1D convolutions at different scales. MT learns spatial features more elaborately to enhance feature representation ability. By combining BGAGCN and MT, our model BGAGCN-MT achieves state-of-the-art accuracy under subject-dependent and subject-independent protocols across three benchmark EEG emotion datasets (SEED, SEED-IV and DREAMER). Notably, our model effectively addresses over-smoothing in graph neural networks and provides an efficient solution to learning spatial relationships of EEG features at different scales.","PeriodicalId":13131,"journal":{"name":"IEEE Transactions on Affective Computing","volume":"15 4","pages":"2042-2054"},"PeriodicalIF":9.6,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140817999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0