21.3.6.2.5 Emotion Recognition, from Other Than Faces

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

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

Song, P., Zheng, W.,
Feature Selection Based Transfer Subspace Learning for Speech Emotion Recognition,
AffCom(11), No. 3, July 2020, pp. 373-382.
IEEE DOI 2008
Speech recognition, Feature extraction, Speech, Emotion recognition, Robustness, Training, Testing, speech emotion recognition 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

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

Li, C., Wang, J., Wang, H., Zhao, M., Li, W., Deng, X.,
Visual-Texual Emotion Analysis With Deep Coupled Video and Danmu Neural Networks,
MultMed(22), No. 6, June 2020, pp. 1634-1646.
IEEE DOI 2005
Danmu, deep multimodal learning, emotion analysis BibRef

Wolfe, H., Peljhan, M., Visell, Y.,
Singing Robots: How Embodiment Affects Emotional Responses to Non-Linguistic Utterances,
AffCom(11), No. 2, April 2020, pp. 284-295.
IEEE DOI 2006
Speech, Robot sensing systems, Psychology, Computers, Visualization, Music, Human robot interaction, non-linguistic utterances, iterative prototyping design BibRef

Tarvainen, J., Laaksonen, J., Takala, T.,
Film Mood and Its Quantitative Determinants in Different Types of Scenes,
AffCom(11), No. 2, April 2020, pp. 313-326.
IEEE DOI 2006
Mood, Estimation, Speech, Visualization, Feature extraction, Cameras, Film, affect, mood, style, content-based analysis BibRef

Nagata, T., Mori, H.,
Defining Laughter Context for Laughter Synthesis with Spontaneous Speech Corpus,
AffCom(11), No. 3, July 2020, pp. 553-559.
IEEE DOI 2008
Speech, Hidden Markov models, Databases, Speech synthesis, Acoustics, Context modeling, Man-machine systems, Laughter, HMM-based speech synthesis BibRef

Colneric, N., Demšar, J.,
Emotion Recognition on Twitter: Comparative Study and Training a Unison Model,
AffCom(11), No. 3, July 2020, pp. 433-446.
IEEE DOI 2008
Twitter, Tagging, Mood, Machine learning, Training, Emotion recognition, Convolutional neural networks, convolutional neural networks BibRef

Ma, Y.[Yue], Ling, C.[Changlong], Wu, J.[Jing],
Exploring the Spatial Distribution Characteristics of Emotions of Weibo Users in Wuhan Waterfront Based on Gender Differences Using Social Media Texts,
IJGI(9), No. 8, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Requardt, A.F.[Alicia F.], Ihme, K.[Klas], Wilbrink, M.[Marc], Wendemuth, A.[Andreas],
Towards affect-aware vehicles for increasing safety and comfort: recognising driver emotions from audio recordings in a realistic driving study,
IET-ITS(14), No. 10, October 2020, pp. 1265-1277.
DOI Link 2009
BibRef

Nguyen, X., Lee, G., Kim, S., Yang, H.,
Audio-Video Based Emotion Recognition Using Minimum Cost Flow Algorithm,
MMVAMTC19(3737-3741)
IEEE DOI 2004
audio signal processing, emotion recognition, feature extraction, graph theory, learning (artificial intelligence), deep learning BibRef

Thao, H.T.P., Herremans, D., Roig, G.,
Multimodal Deep Models for Predicting Affective Responses Evoked by Movies,
CVPM19(1618-1627)
IEEE DOI 2004
audio signal processing, emotion recognition, feature extraction, image sequences, learning (artificial intelligence), neural networks 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

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

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

Abadi, M.K., Correa, J.A.M., Wache, J., Yang, H.[Heng], Patras, I., Sebe, N.,
Inference of personality traits and affect schedule by analysis of spontaneous reactions to affective videos,
FG15(1-8)
IEEE DOI 1508
electrocardiography BibRef

Prasomphan, S.,
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.,
Contribution of Telugu vowels in identifying emotions,
ICAPR15(1-6)
IEEE DOI 1511
Gaussian processes BibRef

Fernández, X.[Ximena], García, R.[Rosana], Ferreira, E.[Enrique], Menéndez, J.[Juan],
Classification of Basic Human Emotions from Electroencephalography Data,
CIARP15(108-115).
Springer DOI 1511
BibRef

Peng, K.C.[Kuan-Chuan], Chen, T.H.[Tsu-Han], Sadovnik, A.[Amir], Gallagher, A.[Andrew],
A mixed bag of emotions: Model, predict, and transfer emotion distributions,
CVPR15(860-868)
IEEE DOI 1510
BibRef

Wang, W.[Weiyi], Athanasopoulos, G.[Georgios], Patsis, G.[Georgios], Enescu, V.[Valentin], Sahli, H.[Hichem],
Real-Time Emotion Recognition from Natural Bodily Expressions in Child-Robot Interaction,
ACVR14(424-435).
Springer DOI 1504
BibRef

Liu, M.M.[Meng-Meng], Chen, H.[Hui], Li, Y.[Yang], Zhang, F.J.[Feng-Jun],
Emotional Tone-Based Audio Continuous Emotion Recognition,
MMMod15(II: 470-480).
Springer DOI 1501
BibRef

Deng, J.[Jun], Zhang, Z.[Zixing], Schuller, B.[Bjorn],
Linked Source and Target Domain Subspace Feature Transfer Learning -- Exemplified by Speech Emotion Recognition,
ICPR14(761-766)
IEEE DOI 1412
Artificial neural networks BibRef

Yuncu, E.[Enes], Hacihabiboglu, H.[Huseyin], Bozsahin, C.[Cem],
Automatic Speech Emotion Recognition Using Auditory Models with Binary Decision Tree and SVM,
ICPR14(773-778)
IEEE DOI 1412
Databases BibRef

Roffo, G.[Giorgio], Giorgetta, C.[Cinzia], Ferrario, R.[Roberta], Cristani, M.[Marco],
Just the Way You Chat: Linking Personality, Style and Recognizability in Chats,
HBU14(30-41).
Springer DOI 1411
BibRef

Wei, H.[Haolin], Monaghan, D.S.[David S.], O'Connor, N.E.[Noel E.], Scanlon, P.[Patricia],
A New Multi-modal Dataset for Human Affect Analysis,
HBU14(42-51).
Springer DOI 1411
Dataset, Human Affect. BibRef

Joo, J.[Jungseock], Li, W.X.[Wei-Xin], Steen, F.F.[Francis F.], Zhu, S.C.[Song-Chun],
Visual Persuasion: Inferring Communicative Intents of Images,
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)
IEEE DOI 1408
acoustic signal processing BibRef

Albornoz, E.M.[E. Marcelo], Sánchez-Gutiérrez, M.E.[Máximo E.], Martinez-Licona, F.[Fabiola], Rufiner, H.L.[H. Leonardo], Goddard, J.[John],
Spoken Emotion Recognition Using Deep Learning,
CIARP14(104-111).
Springer DOI 1411
BibRef
Earlier: A2, A1, A3, A4, A5:
Deep Learning for Emotional Speech Recognition,
MCPR14(311-320).
Springer DOI 1407
BibRef

Lombardo, V.[Vincenzo], Pizzo, A.[Antonio],
Modeling and Visualization of Drama Heritage,
MM4CH13(288-297).
Springer DOI 1309
BibRef

Liu, X.N.[Xiang-Ning], Ji, Y.[Yunong], Akahane, K., Sato, M.,
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)
IEEE DOI 1309
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)
IEEE DOI 1309
emotion recognition BibRef

Zheng, W.M.[Wen-Ming], Zhou, X.Y.[Xiao-Yan],
Speech emotion recognition based on kernel reduced-rank regression,
ICPR12(1972-1976).
WWW Link. 1302
BibRef

Shibata, T.[Tatsuya], Kijima, Y.[Yohei],
Emotion recognition modeling of sitting postures by using pressure sensors and accelerometers,
ICPR12(1124-1127).
WWW Link. 1302
BibRef

Rahman, A.K.M.M.[A.K.M. Mahbubur], Tanveer, M.I.[M. Iftekhar], Anam, A.I.[Asm Iftekhar], Yeasin, M.[Mohammed],
IMAPS: A smart phone based real-time framework for prediction of affect in natural dyadic conversation,
VCIP12(1-6).
IEEE DOI 1302
BibRef

Benuš, Š.[Štefan], Rusko, M.[Milan],
Prosodic Characteristics and Emotional Meanings of Slovak Hot-Spot Words,
COST08(18-27).
Springer DOI 0810
BibRef

Sita, K.K.[Kumari K.], Suhasini, S., Mohd., Z.P.S.[Zaheer Parvez Shaik],
Statistical estimation of emotions in speech notes by featured term analogy,
ICIIP11(1-6).
IEEE DOI 1112
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Computing Emotion Awareness Through Facial Electromyography,
CVHCI06(52-63).
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Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Facial Expressions, Overviews, Surveys, Data .

Last update:Sep 28, 2020 at 12:04:43