21.3.6.2.3 Emotion Recognition, from Other Than Faces

Turchet, L., Rodà, A.,
Emotion Rendering in Auditory Simulations of Imagined Walking Styles,
AffCom(8), No. 2, April 2017, pp. 241-253.
IEEE DOI 1706
Context, Emotion recognition, Engines, Footwear, Legged locomotion, Music, Production, Emotion rendering, footstep sounds, walking BibRef

Turchet, L., Zanotto, D., Minto, S., Rodà, A., Agrawal, S.K.,
Emotion Rendering in Plantar Vibro-Tactile Simulations of Imagined Walking Styles,
AffCom(8), No. 3, July 2017, pp. 340-354.
IEEE DOI 1709
Engines, Foot, Footwear, Haptic interfaces, Legged locomotion, Rendering (computer graphics), Vibrations, Emotion rendering, SoleSounds, footstep sounds, walking BibRef

Hariharan, A., Adam, M.T.P.,
Blended Emotion Detection for Decision Support,
HMS(45), No. 4, August 2015, pp. 510-517.
IEEE DOI 1506
Accuracy BibRef

Mone, G.[Gregory],
Sensing Emotions,
CACM(59), No. 9, September 2015, pp. 15-16.
DOI Link 1509
News story on affective computing. BibRef

Wang, X., Jia, J., Tang, J., Wu, B., Cai, L., Xie, L.,
Modeling Emotion Influence in Image Social Networks,
AffCom(6), No. 3, July 2015, pp. 286-297.
IEEE DOI 1509
Analytical models BibRef

Liang, R.[Ruiyu], Tao, H.[Huawei], Tang, G.C.[Gui-Chen], Wang, Q.Y.[Qing-Yun], Zhao, L.[Li],
A Salient Feature Extraction Algorithm for Speech Emotion Recognition,
IEICE(E98-D), No. 9, September 2015, pp. 1715-1718.
WWW Link. 1509
BibRef

Trentin, E.[Edmondo], Scherer, S.[Stefan], Schwenker, F.[Friedhelm],
Emotion recognition from speech signals via a probabilistic echo-state network,
PRL(66), No. 1, 2015, pp. 4-12.
Elsevier DOI 1511
Emotion recognition BibRef

Kachele, M.[Markus], Zharkov, D.[Dimitrij], Meudt, S.[Sascha], Schwenker, F.[Friedhelm],
Prosodic, Spectral and Voice Quality Feature Selection Using a Long-Term Stopping Criterion for Audio-Based Emotion Recognition,
ICPR14(803-808)
IEEE DOI 1412
Accuracy BibRef

Nardelli, M., Valenza, G., Greco, A., Lanata, A., Scilingo, E.P.,
Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability,
AffCom(6), No. 4, October 2015, pp. 385-394.
IEEE DOI 1512
Acoustics BibRef

Wang, S., Wang, J., Wang, Z., Ji, Q.,
Multiple Emotion Tagging for Multimedia Data by Exploiting High-Order Dependencies Among Emotions,
MultMed(17), No. 12, December 2015, pp. 2185-2197.
IEEE DOI 1512
Databases BibRef

Kim, J.C., Clements, M.A.,
Multimodal Affect Classification at Various Temporal Lengths,
AffCom(6), No. 4, October 2015, pp. 371-384.
IEEE DOI 1512
Audio-visual systems BibRef

Hoey, J.[Jesse], Schröder, T.[Tobias], Alhothali, A.[Areej],
Affect control processes: Intelligent affective interaction using a partially observable Markov decision process,
AI(230), No. 1, 2016, pp. 134-172.
Elsevier DOI 1512
Affect BibRef

Rao, Y.H.[Yang-Hui],
Contextual Sentiment Topic Model for Adaptive Social Emotion Classification,
IEEE_Int_Sys(31), No. 1, January 2016, pp. 41-47.
IEEE DOI 1602
emotion recognition BibRef

Liu, K., Tolins, J., Fox Tree, J.E., Neff, M., Walker, M.A.,
Two Techniques for Assessing Virtual Agent Personality,
AffCom(7), No. 1, January 2016, pp. 94-105.
IEEE DOI 1603
Electronic mail BibRef

Cambria, E.,
Affective Computing and Sentiment Analysis,
IEEE_Int_Sys(31), No. 2, March 2016, pp. 102-107.
IEEE DOI 1604
Affective computing BibRef

Zong, Y., Zheng, W., Zhang, T., Huang, X.,
Cross-Corpus Speech Emotion Recognition Based on Domain-Adaptive Least-Squares Regression,
SPLetters(23), No. 5, May 2016, pp. 585-589.
IEEE DOI 1604
emotion recognition BibRef

Yan, J., Zheng, W., Xu, Q., Lu, G., Li, H., Wang, B.,
Sparse Kernel Reduced-Rank Regression for Bimodal Emotion Recognition From Facial Expression and Speech,
MultMed(18), No. 7, July 2016, pp. 1319-1329.
IEEE DOI 1608
emotion recognition BibRef

Tao, H.[Huawei], Liang, R.[Ruiyu], Zha, C.[Cheng], Zhang, X.[Xinran], Zhao, L.[Li],
Spectral Features Based on Local Hu Moments of Gabor Spectrograms for Speech Emotion Recognition,
IEICE(E99-D), No. 8, August 2016, pp. 2186-2189.
WWW Link. 1608
BibRef

Zhalehpour, S.[Sara], Akhtar, Z.[Zahid], Erdem, C.E.[Cigdem Eroglu],
Multimodal emotion recognition based on peak frame selection from video,
SIViP(10), No. 5, May 2016, pp. 827-834.
WWW Link. 1608
BibRef

Gong, J., Asare, P., Qi, Y., Lach, J.,
Piecewise Linear Dynamical Model for Action Clustering from Real-World Deployments of Inertial Body Sensors,
AffCom(7), No. 3, July 2016, pp. 231-242.
IEEE DOI 1609
Emotion recognition BibRef

Wen, H.W.[Hong-Wei], Liu, Y.[Yue], Rekik, I.[Islem], Wang, S.P.[Sheng-Pei], Chen, Z.Q.[Zhi-Qiang], Zhang, J.S.[Ji-Shui], Zhang, Y.[Yue], Peng, Y.[Yun], He, H.G.[Hui-Guang],
Multi-modal multiple kernel learning for accurate identification of Tourette syndrome children,
PR(63), No. 1, 2017, pp. 601-611.
Elsevier DOI 1612
Tourette syndrome BibRef

Bandhakavi, A.[Anil], Wiratunga, N.[Nirmalie], Massie, S.[Stewart], Padmanabhan, D.[Deepak],
Lexicon Generation for Emotion Detection from Text,
IEEE_Int_Sys(32), No. 1, January 2017, pp. 102-108.
IEEE DOI 1702
Emotion recognition BibRef

Bandhakavi, A.[Anil], Wiratunga, N.[Nirmalie], Padmanabhan, D.[Deepak], Massie, S.[Stewart],
Lexicon based feature extraction for emotion text classification,
PRL(93), No. 1, 2017, pp. 133-142.
Elsevier DOI 1706
Emotion, classification BibRef

Albornoz, E.M., Milone, D.H.,
Emotion recognition in never-seen languages using a novel ensemble method with emotion profiles,
AffCom(8), No. 1, January 2017, pp. 43-53.
IEEE DOI 1703
Cultural differences BibRef

Likforman-Sulem, L., Esposito, A., Faundez-Zanuy, M., Clémençon, S., Cordasco, G.,
EMOTHAW: A Novel Database for Emotional State Recognition From Handwriting and Drawing,
HMS(47), No. 2, April 2017, pp. 273-284.
IEEE DOI 1704
Atmospheric measurements BibRef

Stratou, G.[Giota], Morency, L.P.[Louis-Philippe],
MultiSense: Context-Aware Nonverbal Behavior Analysis Framework: A Psychological Distress Use Case,
AffCom(8), No. 2, April 2017, pp. 190-203.
IEEE DOI 1706
Affective computing, Computer architecture, Context, Pipelines, Psychology, Real-time systems, Synchronization, MultiSense, automatic distress assessment, behavior quantification, framework for multimodal behavioral understanding, system, for, affective, computing BibRef

Venek, V.[Verena], Scherer, S.[Stefan], Morency, L.P.[Louis-Philippe], Rizzo, A.S.[Albert Skip], Pestian, J.[John],
Adolescent Suicidal Risk Assessment in Clinician-Patient Interaction,
AffCom(8), No. 2, April 2017, pp. 204-215.
IEEE DOI 1706
Feature extraction, Interviews, Pediatrics, Repeaters, Risk management, Speech, Support vector machines, Behavior analytics, clinician-patient interaction, hierarchical classifiers, ubiquitous questions, youth, suicide BibRef

Kim, M., Doh, Y.Y.,
Computational Modeling of Players: Emotional Response Patterns to the Story Events of Video Games,
AffCom(8), No. 2, April 2017, pp. 216-227.
IEEE DOI 1706
Analytical models, Computational modeling, Games, Investment, Mathematical model, Media, Predictive models, Computational emotion model, player experience, player modeling, video, game, narrative BibRef

Palo, H.K.[Hemanta Kumar], Chandra, M.[Mahesh], Mohanty, M.N.[Mihir Narayan],
Emotion recognition using MLP and GMM for Oriya language,
IJCVR(7), No. 4, 2017, pp. 426-442.
DOI Link 1708
BibRef

Goshvarpour, A.[Ateke], Daneshvar, S.[Sabalan],
Discrimination between different emotional states based on the chaotic behavior of galvanic skin responses,
SIViP(11), No. 7, October 2017, pp. 1347-1355.
Springer DOI 1708
BibRef

Guendil, Z.[Zied], Lachiri, Z.[Zied], Maaoui, C.[Choubeila],
Computational framework for emotional VAD prediction using regularized Extreme Learning Machine,
MultInfoRetr(6), No. 3, September 2017, pp. 251-261.
Springer DOI 1708
BibRef

Mariooryad, S.[Soroosh], Busso, C.[Carlos],
Facial Expression Recognition in the Presence of Speech Using Blind Lexical Compensation,
AffCom(7), No. 4, October 2016, pp. 346-359.
IEEE DOI 1612
BibRef
Earlier:
Feature and model level compensation of lexical content for facial emotion recognition,
FG13(1-6)
IEEE DOI 1309
BibRef
Earlier:
Factorizing speaker, lexical and emotional variabilities observed in facial expressions,
ICIP12(2605-2608).
IEEE DOI 1302
compensation Bilinear models BibRef

Mariooryad, S.[Soroosh], Busso, C.[Carlos],
Exploring Cross-Modality Affective Reactions for Audiovisual Emotion Recognition,
AffCom(4), No. 2, 2013, pp. 183-196.
IEEE DOI 1307
Entrainment BibRef

Mariooryad, S., Busso, C.,
Correcting Time-Continuous Emotional Labels by Modeling the Reaction Lag of Evaluators,
AffCom(6), No. 2, April 2015, pp. 97-108.
IEEE DOI 1507
emotion recognition BibRef

Busso, C., Mariooryad, S., Metallinou, A., Narayanan, S.,
Iterative Feature Normalization Scheme for Automatic Emotion Detection from Speech,
AffCom(4), No. 4, October 2013, pp. 386-397.
IEEE DOI 1406
emotion recognition BibRef

Deng, J., Zhang, Z., Eyben, F., Schuller, B.,
Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition,
SPLetters(21), No. 9, Sept 2014, pp. 1068-1072.
IEEE DOI 1406
Emotion recognition BibRef

Deng, J., Xu, X., Zhang, Z., Frühholz, S., Schuller, B.,
Universum Autoencoder-Based Domain Adaptation for Speech Emotion Recognition,
SPLetters(24), No. 4, April 2017, pp. 500-504.
IEEE DOI 1704
Databases BibRef

Mencattini, A., Martinelli, E., Ringeval, F., Schuller, B., Natale, C.D.,
Continuous Estimation of Emotions in Speech by Dynamic Cooperative Speaker Models,
AffCom(8), No. 3, July 2017, pp. 314-327.
IEEE DOI 1709
Data models, Databases, Emotion recognition, Gold, Predictive models, Speech, Standards, Speech emotion recognition, cooperative regression model, naturalistic emotional display BibRef

Naji, M.[Mohsen], Firoozabadi, M.[Mohammd], Azadfallah, P.[Parviz],
Emotion classification during music listening from forehead biosignals,
SIViP(9), No. 6, September 2015, pp. 1365-1375.
WWW Link. 1509
BibRef

Savran, A., Cao, H.[Houwei], Nenkova, A., Verma, R.,
Temporal Bayesian Fusion for Affect Sensing: Combining Video, Audio, and Lexical Modalities,
Cyber(45), No. 9, September 2015, pp. 1927-1941.
IEEE DOI 1509
Bayes methods BibRef

Zhao, R., Mao, K.,
Cyberbullying Detection Based on Semantic-Enhanced Marginalized Denoising Auto-Encoder,
AffCom(8), No. 3, July 2017, pp. 328-339.
IEEE DOI 1709
Analytical models, Feature extraction, Media, Noise reduction, Numerical models, Robustness, Semantics, Cyberbullying detection, representation learning, stacked denoising autoencoders, text mining, word, embedding BibRef

Georgakis, C.[Christos], Panagakis, Y.[Yannis], Zafeiriou, S.P.[Stefanos P.], Pantic, M.[Maja],
The Conflict Escalation Resolution (CONFER) Database,
IVC(65), No. 1, 2017, pp. 37-48.
Elsevier DOI 1709
BibRef
Earlier: A2, A3, A4, Only:
Audiovisual Conflict Detection in Political Debates,
FacBeh14(306-314).
Springer DOI 1504
Automatic conflict analysis BibRef

Fortin, P.E., Cooperstock, J.R.,
Laughter and Tickles: Toward Novel Approaches for Emotion and Behavior Elicitation,
AffCom(8), No. 4, October 2017, pp. 508-521.
IEEE DOI 1712
Auditory system, Haptic interfaces, Physiology, Psychology, Temperature sensors, Vibrations, Visualization, Laughter, tickling BibRef

Li, M., Lu, Q., Long, Y., Gui, L.,
Inferring Affective Meanings of Words from Word Embedding,
AffCom(8), No. 4, October 2017, pp. 443-456.
IEEE DOI 1712
Affective computing, Computational modeling, Context, Crowdsourcing, Knowledge based systems, Manuals, Semantics, word embedding BibRef

Cambria, E., Hussain, A., Vinciarelli, A.,
Affective Reasoning for Big Social Data Analysis,
AffCom(8), No. 4, October 2017, pp. 426-427.
IEEE DOI 1712
BibRef

Zhang, S.Q.[Shi-Qing], Zhang, S.L.[Shi-Liang], Huang, T.J.[Tie-Jun], Gao, W.[Wen],
Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching,
MultMed(20), No. 6, June 2018, pp. 1576-1590.
IEEE DOI 1805
Acoustics, Convolution, Emotion recognition, Feature extraction, Neural networks, Speech, Speech recognition, Lp-norm pooling, feature learning BibRef

Shepstone, S.E., Tan, Z.H., Jensen, S.H.,
Audio-Based Granularity-Adapted Emotion Classification,
AffCom(9), No. 2, April 2018, pp. 176-190.
IEEE DOI 1806
Context, Emotion recognition, Facsimile, Motion pictures, Speech, Support vector machines, Visualization, Emotion, SVM, affect, multidimensional scaling BibRef

Sahoo, S., Routray, A.,
Detecting Aggression in Voice Using Inverse Filtered Speech Features,
AffCom(9), No. 2, April 2018, pp. 217-226.
IEEE DOI 1806
Cameras, Feature extraction, Interviews, Psychology, Speech, Speech processing, TV, Aggression detection, speech inverse filtering BibRef

Zhao, M.M.[Ming-Min], Adib, F.[Fadel], Katabi, D.[Dina],
Emotion Recognition Using Wireless Signals,
CACM(61), No. 9, September 2018, pp. 91-100.
DOI Link 1809
From RF signals reflected of the body. From detecting heartbeats. BibRef

Chen, M.Y.[Ming-Yi], He, X.J.[Xuan-Ji], Yang, J.[Jing], Zhang, H.[Han],
3-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition,
SPLetters(25), No. 10, October 2018, pp. 1440-1444.
IEEE DOI 1810
convolution, emotion recognition, feature extraction, feedforward neural nets, recurrent neural nets, speech emotion recognition (SER) BibRef

Zhang, S., Zhang, S., Huang, T., Gao, W., Tian, Q.,
Learning Affective Features With a Hybrid Deep Model for Audio-Visual Emotion Recognition,
CirSysVideo(28), No. 10, October 2018, pp. 3030-3043.
IEEE DOI 1811
Feature extraction, Emotion recognition, Visualization, Image segmentation, Machine learning, Databases, Convolution, multimodality fusion BibRef

Rzeszewski, M.[Michal], Luczys, P.[Piotr],
Care, Indifference and Anxiety: Attitudes toward Location Data in Everyday Life,
IJGI(7), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Liu, Y., Yu, M., Zhao, G., Song, J., Ge, Y., Shi, Y.,
Real-Time Movie-Induced Discrete Emotion Recognition from EEG Signals,
AffCom(9), No. 4, October 2018, pp. 550-562.
IEEE DOI 1812
Emotion recognition, Real-time systems, Electroencephalography, Brain models, Films, Support vector machines, Affective computing, movie BibRef

Belkaid, M., Cuperlier, N., Gaussier, P.,
Autonomous Cognitive Robots Need Emotional Modulations: Introducing the eMODUL Model,
SMCS(49), No. 1, January 2019, pp. 206-215.
IEEE DOI 1901
Cognition, Robots, Appraisal, Task analysis, Modulation, Computational modeling, Psychology, Emotional modulations, robot emotions BibRef

Jing, S.L.[Shao-Ling], Mao, X.[Xia], Chen, L.J.[Li-Jiang],
Automatic speech discrete labels to dimensional emotional values conversion method,
IET-Bio(8), No. 2, March 2019, pp. 168-176.
DOI Link 1902
BibRef

Xu, X., Deng, J., Coutinho, E., Wu, C., Zhao, L., Schuller, B.W.,
Connecting Subspace Learning and Extreme Learning Machine in Speech Emotion Recognition,
MultMed(21), No. 3, March 2019, pp. 795-808.
IEEE DOI 1903
emotion recognition, feedforward neural nets, graph theory, human computer interaction, regression analysis, spectral regression BibRef

Ye, J., Li, J., Newman, M.G., Adams, R.B., Wang, J.Z.,
Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data,
AffCom(10), No. 1, January 2019, pp. 115-128.
IEEE DOI 1903
Reliability, Data models, Probabilistic logic, Computational modeling, Psychology, Crowdsourcing, visual stimuli BibRef

Li, X., Peng, Q., Sun, Z., Chai, L., Wang, Y.,
Predicting Social Emotions from Readers' Perspective,
AffCom(10), No. 2, April 2019, pp. 255-264.
IEEE DOI 1906
Statistical analysis, Predictive models, Feature extraction, Social network services, Semantics, Internet, complex network BibRef

Song, P.,
Transfer Linear Subspace Learning for Cross-Corpus Speech Emotion Recognition,
AffCom(10), No. 2, April 2019, pp. 265-275.
IEEE DOI 1906
Speech, Speech recognition, Emotion recognition, Training, Testing, Principal component analysis, Algorithm design and analysis, dimensionality reduction BibRef

Jia, J.[Jia], Zhou, S.P.[Su-Ping], Yin, Y.F.[Yu-Feng], Wu, B.[Boya], Chen, W.[Wei], Meng, F.[Fanbo], Wang, Y.F.[Yan-Feng],
Inferring Emotions From Large-Scale Internet Voice Data,
MultMed(21), No. 7, July 2019, pp. 1853-1866.
IEEE DOI 1906
Feature extraction, Speech recognition, Emotion recognition, Acoustics, Hidden Markov models, Neural networks, long short-term memory BibRef

Ren, Z.[Zhu], Jia, J.[Jia], Cai, L.H.[Lian-Hong], Zhang, K.[Kuo], Tang, J.[Jie],
Learning to Infer Public Emotions from Large-Scale Networked Voice Data,
MMMod14(I: 327-339).
Springer DOI 1405
BibRef

Avots, E.[Egils], Sapinski, T.[Tomasz], Bachmann, M.[Maie], Kaminska, D.[Dorota],
Audiovisual emotion recognition in wild,
MVA(30), No. 5, July 2019, pp. 975-985.
Springer DOI 1907
BibRef

Xie, Y.[Yue], Liang, R.[Ruiyu], Liang, Z.L.[Zhen-Lin], Zhao, L.[Li],
Attention-Based Dense LSTM for Speech Emotion Recognition,
IEICE(E102-D), No. 7, July 2019, pp. 1426-1429.
WWW Link. 1907
BibRef

Alarcão, S.M., Fonseca, M.J.,
Emotions Recognition Using EEG Signals: A Survey,
AffCom(10), No. 3, July 2019, pp. 374-393.
IEEE DOI 1909
Electroencephalography, Electrodes, Electric potential, Emotion recognition, Feature extraction, Brain, recognition BibRef

Zheng, W., Zhu, J., Lu, B.,
Identifying Stable Patterns over Time for Emotion Recognition from EEG,
AffCom(10), No. 3, July 2019, pp. 417-429.
IEEE DOI 1909
Electroencephalography, Emotion recognition, Stability analysis, Feature extraction, Brain modeling, Time-frequency analysis, extreme learning machine BibRef

Basnet, R.[Ramesh], Islam, M.T.[Mohammad Tariqul], Howlader, T.[Tamanna], Rahman, S.M.M.[S. M. Mahbubur], Hatzinakos, D.[Dimitrios],
Estimation of affective dimensions using CNN-based features of audiovisual data,
PRL(128), 2019, pp. 290-297.
Elsevier DOI 1912
Convolutional neural network, Affective features, Emotional dimensions BibRef

Singh, B.K.[Bikesh Kumar], Khare, A.[Ankur], Soni, A.K.[Abhishek Kumar], Kumar, A.[Arun],
Electroencephalography-based classification of human emotion: a hybrid strategy in machine learning paradigm,
IJCVR(9), No. 6, 2019, pp. 583-598.
DOI Link 1912
BibRef

Luo, Y.[Yu], Ye, J.B.[Jian-Bo], Adams, Jr., R.B.[Reginald B.], Li, J.[Jia], Newman, M.G.[Michelle G.], Wang, J.Z.[James Z.],
ARBEE: Towards Automated Recognition of Bodily Expression of Emotion in the Wild,
IJCV(128), No. 1, January 2020, pp. 1-25.
Springer DOI 2002
BibRef

Gosztolya, G., Grósz, T., Tóth, L.,
Social Signal Detection by Probabilistic Sampling DNN Training,
AffCom(11), No. 1, January 2020, pp. 164-177.
IEEE DOI 2003
Training, Probabilistic logic, Hidden Markov models, Task analysis, Training data, Neural networks, Speech recognition, laughter detection BibRef

Guo, L.[Lili], Wang, L.B.[Long-Biao], Dang, J.W.[Jian-Wu], Liu, Z.L.[Zhi-Lei], Guan, H.T.[Hao-Tian],
Speaker-aware Speech Emotion Recognition by Fusing Amplitude and Phase Information,
MMMod20(I:14-25).
Springer DOI 2003
BibRef

Zhou, S.P.[Su-Ping], Jia, J.[Jia], Zhang, L.[Long], Wang, Y.F.[Yan-Feng], Chen, W.[Wei], Meng, F.[Fanbo], Yu, F.[Fei], Shen, J.L.[Jia-Lie],
Inferring Emphasis for Real Voice Data: An Attentive Multimodal Neural Network Approach,
MMMod20(II:52-62).
Springer DOI 2003
BibRef

Yu, J., Zheng, W.,
Learning Prediction of Emotional Change on Behaviors,
ICIP19(999-1003)
IEEE DOI 1910
Emotional change prediction, multimodal fusion, deep network BibRef

Nguyen, T., Zhou, T., Potter, T., Zou, L., Zhang, Y.,
The Cortical Network of Emotion Regulation: Insights From Advanced EEG-fMRI Integration Analysis,
MedImg(38), No. 10, October 2019, pp. 2423-2433.
IEEE DOI 1910
Functional magnetic resonance imaging, Electroencephalography, Spatiotemporal phenomena, Image reconstruction, Brain modeling, causal brain network BibRef

Bang, E.[Eun_Seo], Yildirim, C.[Caglar],
Virtually Empathetic?: Examining the Effects of Virtual Reality Storytelling on Empathy,
VAMR18(I: 290-298).
Springer DOI 1807
BibRef

Drnec, K.[Kim], Gremillion, G.[Greg], Donavanik, D.[Daniel], Canady, J.D.[Jonroy D.], Atwater, C.[Corey], Carter, E.[Evan], Haynes, B.A.[Ben A.], Marathe, A.R.[Amar R.], Metcalfe, J.S.[Jason S.],
The Role of Psychophysiological Measures as Implicit Communication Within Mixed-Initiative Teams,
VAMR18(I: 299-313).
Springer DOI 1807
BibRef

Gong, J.T.[Jiang-Tao], Shi, Y.[Yin], Wang, J.[Jue], Shi, D.[Danqing], Xu, Y.Q.[Ying-Qing],
Escape from the Dark Jungle: A 3D Audio Game for Emotion Regulation,
VAMR18(II: 57-76).
Springer DOI 1807
BibRef

de la Pava, I.[Iván], Álvarez-Meza, A.[Andres], Orozco, A.A.[Alvaro-Angel],
Emotion Assessment by Variability-Based Ranking of Coherence Features from EEG,
CIARP17(203-211).
Springer DOI 1802
BibRef

Wang, F.[Fang], Zhong, S.H.[Sheng-Hua], Peng, J.F.[Jian-Feng], Jiang, J.M.[Jian-Min], Liu, Y.[Yan],
Data Augmentation for EEG-Based Emotion Recognition with Deep Convolutional Neural Networks,
MMMod18(II:82-93).
Springer DOI 1802
BibRef

Samanta, P.[Pallabi], Bhattacharya, D.[Diptendu], De, A.[Amiyangshu], Ghosh, L.[Lidia], Konar, A.[Amit],
Music-Induced Emotion Classification from the Prefrontal Hemodynamics,
PReMI17(289-295).
Springer DOI 1711
BibRef

Sharma, S.[Shikhar], Kumar, P.[Piyush], Kumar, K.[Krishan],
LEXER: LEXicon Based Emotion AnalyzeR,
PReMI17(373-379).
Springer DOI 1711
BibRef

Dey, A.[Atanu], Jenamani, M.[Mamata], Thakkar, J.J.[Jitesh J.],
Lexical TF-IDF: An n-gram Feature Space for Cross-Domain Classification of Sentiment Reviews,
PReMI17(380-386).
Springer DOI 1711
BibRef

Wickramaarachchi, W.U., Kariapper, R.K.A.R.,
An approach to get overall emotion from comment text towards a certain image uploaded to social network using Latent Semantic Analysis,
ICIVC17(788-792)
IEEE DOI 1708
Automobiles, Electric shock, emotion identification, latent semantic analysis, natural language processing, social network, text, processing BibRef

Pitas, I., Kotsia, I., Martin, O., Macq, B.,
The eNTERFACE-05 Audio-Visual Emotion Database,
ICDEW06(8).
IEEE DOI BibRef 0600

Aydin, B., Kindiroglu, A.A., Aran, O., Akarun, L.,
Automatic personality prediction from audiovisual data using random forest regression,
ICPR16(37-42)
IEEE DOI 1705
Correlation, Feature extraction, Social network services, Speech, Standards, Time-frequency analysis, Visualization BibRef

Chakraborty, R., Pandharipande, M., Kopparapu, S.K.,
Spontaneous speech emotion recognition using prior knowledge,
ICPR16(2866-2871)
IEEE DOI 1705
Context, Emotion recognition, Feature extraction, Knowledge based systems, Pragmatics, Speech, Speech recognition, Emotion recognition, call center audio, knowledge-based framework, non-acted emotion, spontaneous, speech BibRef

Cirakman, O., Gunsel, B.,
Online speaker emotion tracking with a dynamic state transition model,
ICPR16(307-312)
IEEE DOI 1705
Attenuation, Computational modeling, Feature extraction, Psychoacoustic models, Solid modeling, Speech, Training, affective computing, emotion recognition, particle, filter BibRef

Moro, A.D.[Angelo D.], Quintero, C.[Christian], Sarmiento, W.J.[Wilson J.],
Narrative Approach to Assess Fear of Heights in Virtual Environments,
ISVC16(I: 691-700).
Springer DOI 1701
BibRef

Chandrasekaran, A.[Arjun], Vijayakumar, A.K.[Ashwin K.], Antol, S.[Stanislaw], Bansal, M.[Mohit], Batra, D.[Dhruv], Zitnick, C.L.[C. Lawrence], Parikh, D.[Devi],
We are Humor Beings: Understanding and Predicting Visual Humor,
CVPR16(4603-4612)
IEEE DOI 1612
BibRef

Impett, L.[Leonardo], Süsstrunk, S.[Sabine],
Pose and Pathosformel in Aby Warburg's Bilderatlas,
CVAA16(I: 888-902).
Springer DOI 1611
the repeatable formula for the expression of emotion, through the depiction of human pose in art. Crowdsource annotated art. BibRef

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FG15(1-8)
IEEE DOI 1508
electrocardiography BibRef

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Improvement of speech emotion recognition with neural network classifier by using speech spectrogram,
WSSIP15(73-76)
IEEE DOI 1603
emotion recognition BibRef

Shashidhar, K.G., Shivakranthi, B., Rao, K.S., Ramteke, P.B.,
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ICAPR15(1-6)
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Gaussian processes BibRef

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CIARP15(108-115).
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IEEE DOI 1510
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ICPR14(761-766)
IEEE DOI 1412
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Automatic Speech Emotion Recognition Using Auditory Models with Binary Decision Tree and SVM,
ICPR14(773-778)
IEEE DOI 1412
Databases BibRef

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CVPR14(216-223)
IEEE DOI 1409
Communicative Intents BibRef

Kaveeta, V., Patanukhom, K.,
Emotional Speech Recognition Using Acoustic Models of Decomposed Component Words,
ACPR13(115-119)
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acoustic signal processing BibRef

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Spoken Emotion Recognition Using Deep Learning,
CIARP14(104-111).
Springer DOI 1411
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MCPR14(311-320).
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Lombardo, V.[Vincenzo], Pizzo, A.[Antonio],
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MM4CH13(288-297).
Springer DOI 1309
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Proposal on an image haptization system based on emotional effects of color,
FG13(1-6)
IEEE DOI 1309
emotion recognition BibRef

Abadi, M.K., Kia, M., Subramanian, R., Avesani, P., Sebe, N.,
Decoding affect in videos employing the MEG brain signal,
FG13(1-6)
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magnetoencephalography BibRef

Liu, P.[Ping], Han, S.H.[Shiz-Hong], Tong, Y.[Yan],
Improving facial expression analysis using histograms of Log-Transformed Nonnegative Sparse Representation with a Spatial Pyramid Structure,
FG13(1-7)
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Speech emotion recognition based on kernel reduced-rank regression,
ICPR12(1972-1976).
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IASP11(670-672).
IEEE DOI 1112
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FG11(821-826).
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FG11(780-785).
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FG11(786-791).
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ICPR10(1614-1617).
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ICPR10(4488-4491).
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Biosignal Based Emotion Analysis of Human-Agent Interactions,
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Pao, T.L.[Tsang-Long], Chen, Y.T.[Yu-Te], Yeh, J.H.[Jun-Heng], Li, P.J.[Pei-Jia],
Mandarin Emotional Speech Recognition Based on SVM and NN,
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You, M.Y.[Ming-Yu], Chen, C.[Chun], Bu, J.J.[Jia-Jun], Liu, J.[Jia], Tao, J.H.[Jian-Hua],
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van den Broek, E.L.[Egon L.], Schut, M.H.[Marleen H.], Westerink, J.H.D.M.[Joyce H.D.M.], van Herk, J.[Jan], Tuinenbreijer, K.[Kees],
Computing Emotion Awareness Through Facial Electromyography,
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Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Facial Expressions, Overviews, Surveys, Data .

Last update:Mar 29, 2020 at 12:14:19