Multimodal integration-a statistical view

Pattern classification and scene analysis

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Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition

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The logic of typed feature structures

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QuickSet

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Data fusion in robotics and machine intelligence

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The Logic of Typed Feature Structures

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Multimodal learning with transformers: A survey

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Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

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Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning

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Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

Corneanu, C. A., Simon, M. O., Cohn, J. F., & Guerrero, S. E. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2515606 · Cited by 527

Multimodal interaction: A review

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Multimodal Interfaces

Oviatt, S. L. (2007) · CRC Press eBooks · doi:10.1201/9781410615862.ch21 · Cited by 492

Automatic, Dimensional and Continuous Emotion Recognition

Güneş, H., & Pantić, M. (2010) · International Journal of Synthetic Emotions · doi:10.4018/jse.2010101605 · Cited by 443

Bi-modal emotion recognition from expressive face and body gestures

Gunes, H., & Piccardi, M. (2007) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2006.09.007 · Cited by 382

Designing the User Interface for Multimodal Speech and Pen-Based Gesture Applications: State-of-the-Art Systems and Future Research Directions

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Multimodal Interfaces: A Survey of Principles, Models and Frameworks

Dumas, B., Lalanne, D., & Oviatt, S. L. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00437-7_1 · Cited by 308

Taming recognition errors with a multimodal interface

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Gesture interaction in virtual reality

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Perceptual user interfaces: multimodal interfaces that process what comes naturally

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Survey of data management and analysis in disaster situations

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A review of speech-based bimodal recognition

Chibelushi, C. C., Deravi, F., & Mason, J. S. (2002) · IEEE Transactions on Multimedia · doi:10.1109/6046.985551 · Cited by 222

Automatic Temporal Segment Detection and Affect Recognition From Face and Body Display

Gunes, H., & Piccardi, M. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.927269 · Cited by 225

MULTIMODAL INTERFACES THAT PROCESS WHAT COMES NATURALLY

Oviatt, S. L., & Cohen, P. R. (2000) · Cited by 196

Being bored? Recognising natural interest by extensive audiovisual integration for real-life application

Schuller, B. W., Müller, R., Eyben, F., Gast, J., Hörnler, B., Wöllmer, M., Rigoll, G., Hoethker, A., & Konosu, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.02.013 · Cited by 194

Affect Recognition from Face and Body: Early Fusion vs. Late Fusion

Gunes, H., & Piccardi, M. (2006) · 2005 IEEE International Conference on Systems, Man and Cybernetics · doi:10.1109/icsmc.2005.1571679 · Cited by 191

Mutual disambiguation of 3D multimodal interaction in augmented and virtual reality

Kaiser, E., Olwal, A., McGee, D. R., Benko, H., Corradini, A., Li, X., Cohen, P. R., & Feiner, S. (2003) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/958432.958438 · Cited by 179

SketchREAD

Alvarado, C., & Davis, R. (2004) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1029632.1029637 · Cited by 158

Weighted Consensus Clustering

Li, T., & Ding, C. H. Q. (2008) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972788.72 · Cited by 145

Orko: Facilitating Multimodal Interaction for Visual Exploration and Analysis of Networks

Srinivasan, A., & Stasko, J. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2745219 · Cited by 142

Generation and evaluation of user tailored responses in multimodal dialogue

Walker, M. A., Whittaker, S. J., Stent, A., Maloor, P., Moore, J., Johnston, M., & Vasireddy, G. (2004) · Cognitive Science · doi:10.1016/j.cogsci.2004.06.002 · Cited by 125

Understanding vision-based continuous sign language recognition

Aloysius, N., & Geetha, M. K. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08961-z · Cited by 124

Handbook of Research on User Interface Design and Evaluation for Mobile Technology

Lumsden, J. (2008) · IGI Global eBooks · doi:10.4018/978-1-59904-871-0 · Cited by 120

Finite-state multimodal parsing and understanding

Johnston, M. O., & Bangalore, S. (2000) · International Conference on Computational Linguistics · doi:10.3115/990820.990874 · Cited by 116

Advances in robust multimodal interface design

Oviatt, S. L. (2003) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2003.1231179 · Cited by 113

Unobtrusive sleep state measurements in preterm infants – A review

Werth, J., Atallah, L., Andriessen, P., Long, X., Zwartkruis-Pelgrim, E., & Aarts, R. M. (2016) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2016.03.005 · Cited by 106

Automatic Emotion Recognition Using Temporal Multimodal Deep Learning

Nakisa, B., Rastgoo, M. N., Rakotonirainy, A., Maire, F., & Chandran, V. (2020) · IEEE Access · doi:10.1109/access.2020.3027026 · Cited by 101

Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers

Fraser, K., Fors, K. L., Eckerström, M., Öhman, F., & Kokkinakis, D. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00205 · Cited by 99

From the Lab to the Real World: Affect Recognition Using Multiple Cues and Modalities

Güneş, H., Piccardi, M., & Pantić, M. (2008) · doi:10.5772/6180 · Cited by 95

On probabilistic combination of face and gait cues for identification

Shakhnarovich, G., & Darrell, T. (2003) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2002.1004151 · Cited by 90

Implementation and evaluation of a constraint-based multimodal fusion system for speech and 3D pointing gestures

Holzapfel, H., Nickel, K., & Stiefelhagen, R. (2004) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1027933.1027964 · Cited by 88

Coupled hidden conditional random fields for RGB-D human action recognition

Liu, A.-A., Nie, W., Su, Y., Ma, L., Hao, T., & Yang, Z. (2014) · Signal Processing · doi:10.1016/j.sigpro.2014.08.038 · Cited by 87

A multidimensional dynamic time warping algorithm for efficient multimodal fusion of asynchronous data streams

Wöllmer, M., Al-Hames, M., Eyben, F., Schuller, B. W., & Rigoll, G. (2009) · Neurocomputing · doi:10.1016/j.neucom.2009.08.005 · Cited by 87

Multi-view latent variable discriminative models for action recognition

Song, Y., Morency, L., & Davis, R. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247918 · Cited by 81

Recognizing Affect from Linguistic Information in 3D Continuous Space

Schuller, B. W. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.17 · Cited by 69

MFGAN: Multimodal Fusion for Industrial Anomaly Detection Using Attention-Based Autoencoder and Generative Adversarial Network

Qu, X., Liu, Z., Wu, C. Q., Hou, A., Yin, X., & Chen, Z. (2024) · Sensors · doi:10.3390/s24020637 · Cited by 71

Breaking the Robustness Barrier: Recent Progress on the Design of Robust Multimodal Systems

Oviatt, S. L. (2002) · Advances in computers · doi:10.1016/s0065-2458(02)80009-2 · Cited by 62

SketchREAD

Alvarado, C., & Davis, R. (2007) · ACM SIGGRAPH Courses · doi:10.1145/1281500.1281545 · Cited by 59

Image-text dual neural network with decision strategy for small-sample image classification

Zhu, F., Ma, Z., Li, X., Chen, G., Chien, J., Xue, J., & Guo, J. H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.02.099 · Cited by 58

DNN Multimodal Fusion Techniques for Predicting Video Sentiment

Williams, J. A., Comanescu, R., Radu, O., & Tian, L. (2018) · doi:10.18653/v1/w18-3309 · Cited by 56

Multimodality Sensor System for Long-Term Sleep Quality Monitoring

Peng, Y.-T., Lin, C., Sun, M., & Landis, C. A. (2007) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2007.914481 · Cited by 56

Machine vision approach for robotic assembly

Peña‐Cabrera, M., López-Juárez, I., Rìos-Cabrera, R., & Corona‐Castuera, J. (2005) · Assembly Automation · doi:10.1108/01445150510610926 · Cited by 53

Creating tangible interfaces by augmenting physical objects with multimodal language

McGee, D. R., & Cohen, P. R. (2001) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/359784.360305 · Cited by 53

User and context adaptive neural networks for emotion recognition

Caridakis, G., Karpouzis, K. C., & Kollias, S. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.11.043 · Cited by 51

Something from nothing

McGee, D. R., Cohen, P. R., & Wu, L. (2000) · Designing Augmented Reality Environments (DARE) · doi:10.1145/354666.354674 · Cited by 50

Affect recognition from facial movements and body gestures by hierarchical deep spatio-temporal features and fusion strategy

Sun, B., Cao, S., He, J. Y. D., & Yu, L. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.11.021 · Cited by 48

Multimodal speech-gesture interface for handfree painting on a virtual paper using partial recurrent neural networks as gesture recognizer

Corradini, A., & Cohen, P. R. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2002.1007499 · Cited by 45

Dynamically constructed Bayes nets for multi-domain sketch understanding

Alvarado, C., & Davis, R. (2007) · ACM SIGGRAPH Courses · doi:10.1145/1281500.1281544 · Cited by 44

The Paradigm Shift to Multimodality in Contemporary Computer Interfaces

Oviatt, S. L., & Cohen, P. R. (2015) · Synthesis lectures on human-centered informatics · doi:10.2200/s00636ed1v01y201503hci030 · Cited by 60

Multimodal user’s affective state analysis in naturalistic interaction

Caridakis, G., Karpouzis, K. C., Wallace, M., Kessous, L., & Amir, N. (2009) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-009-0030-8 · Cited by 38

Semisupervised learning from different information sources

Li, T., & Ogihara, M. (2004) · Knowledge and Information Systems · doi:10.1007/s10115-004-0155-8 · Cited by 35

Multi-view Generative Adversarial Networks

Chen, M., & Denoyer, L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71246-8_11 · Cited by 33

EMBASSI multimodal assistance for infotainment and service infrastructures

Herfet, T., Kirste, T., & Schnaider, M. (2001) · Computers & Graphics · doi:10.1016/s0097-8493(01)00086-3 · Cited by 33

Fusing Face and Body Display for Bi-modal Emotion Recognition: Single Frame Analysis and Multi-frame Post Integration

Güneş, H., & Piccardi, M. (2005) · Lecture notes in computer science · doi:10.1007/11573548_14 · Cited by 32

Recognition of Emotional States in Natural Human-Computer Interaction

Cowie, R., Douglas‐Cowie, E., Karpouzis, K. C., Caridakis, G., Wallace, M., & Kollias, S. (2008) · Springer eBooks · doi:10.1007/978-3-540-78345-9_6 · Cited by 33

A survey of speech-hand gesture recognition for the development of multimodal interfaces in computer games

Liu, J., & Kavakli, M. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5583252 · Cited by 32

From members to teams to committee-a robust approach to gestural and multimodal recognition

Wu, L., Oviatt, S. L., & Cohen, P. R. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2002.1021897 · Cited by 30

Multimodal Integration - A Biological View

Coen, M. H. (2001) · Cited by 29

Multimodal interface research: a science without borders

Oviatt, S. L. (2000) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2000-464 · Cited by 28

Fusing face and body gesture for machine recognition of emotions

Gunes, H., & Piccardi, M. (2006) · IEEE RO-MAN · doi:10.1109/roman.2005.1513796 · Cited by 28

Multi-domain sketch understanding

Alvarado, C. (2007) · ACM SIGGRAPH Courses · doi:10.1145/1281500.1281518 · Cited by 27

A Salience-Based Approach to Gesture-Speech Alignment.

Eisenstein, J., & Christoudias, C. M. (2004) · Cited by 26

Emotion Recognition in Naturalistic Speech and Language—A Survey

Weninger, F. J., Wöllmer, M., & Schuller, B. W. (2015) · Wiley eBooks · doi:10.1002/9781118910566.ch10 · Cited by 26

Learning and Knowledge-Based Sentiment Analysis in Movie Review Key Excerpts

Schuller, B. W., & Knaup, T. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-18184-9_39 · Cited by 25

Dynamically constructed Bayes nets for multi-domain sketch understanding

Alvarado, C., & Davis, R. (2006) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1185657.1185793 · Cited by 25

Optimization in multimodal interpretation

Chai, J. Y., Hong, P., Zhou, M. X., & Prasov, Z. (2004) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1218955.1218956 · Cited by 25

Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML)

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EMBASSI: Electronic Multimedia and Service Assistance

Hildebrand, A., & Sá, V. J. (2022) · doi:10.24406/publica-fhg-335797 · Cited by 21

WavoID: Robust and Secure Multi-modal User Identification via mmWave-voice Mechanism

Liu, T., Lin, F., Wang, C., Xu, C., Zhang, X., Li, Z., Xu, W., Huang, M.-C., & Ren, K. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586183.3606775 · Cited by 20

Developing an Ambient Intelligent-Based Decision Support System for Production and Control Planning

Gomes, M., Silva, F., Ferraz, F., Silva, A., Analide, C., & Nováis, P. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-53480-0_97 · Cited by 20

Improvement of multimodal gesture and speech recognition performance using time intervals between gestures and accompanying speech

Miki, M., Kitaoka, N., Miyajima, C., Nishino, T., & Takeda, K. (2014) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/1687-4722-2014-2 · Cited by 18

Gene functional classification by semi-supervised learning from heterogeneous data

Li, T., Shenghuo Zhu, B., Li, Q., & Ogihara, M. (2003) · ACM Symposium on Applied Computing · doi:10.1145/952532.952552 · Cited by 18

Web Multimedia Object Classification Using Cross-Domain Correlation Knowledge

Lu, W., Li, J., Li, T., Guo, W., Zhang, H., & Guo, J. H. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2280895 · Cited by 17

An Agent-Based Multimodal Interface for Sketch Interpretation

Azar, S., Couvreur, L., Delfosse, V., Jaspart, B., & Boulanger, C. (2006) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2006.285357 · Cited by 17

Cognitive principles in robust multimodal interpretation

Chai, J. Y., Prasov, Z., & Qu, S. (2008) · Cited by 17

Latent Semantic Analysis for Multimodal User Input With Speech and Gestures

Hui, P. Y., & Meng, H. M. L. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2013.2294586 · Cited by 16

Automatic, Dimensional and Continuous Emotion Recognition

Güneş, H., & Pantić, M. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-1595-3.ch005 · Cited by 18

Multimodal Human-Computer Interaction

Turk, M. (2005) · Springer eBooks · doi:10.1007/0-387-27890-7_16 · Cited by 16

Machine learning in low-level microarray analysis

Rubinstein, B. I. P., McAuliffe, J., Cawley, S. E., Palaniswami, M. S., Ramamohanarao, K., & Speed, T. P. (2003) · ACM SIGKDD Explorations Newsletter · doi:10.1145/980972.980988 · Cited by 16

Multimodal Input

Ruiz, N., Chen, F., & Oviatt, S. L. (2010) · Elsevier eBooks · doi:10.1016/b978-0-12-374825-6.00010-1 · Cited by 15

Integrating Features from Different Sources for Music Information Retrieval

Li, T., Ogihara, M., & Shenghuo Zhu, B. (2006) · Proceedings · doi:10.1109/icdm.2006.89 · Cited by 15

EMBASSI

Kirste, T., Herfet, T., & Schnaider, M. (2001) · EC/NSF Workshop on Universal Accessibility of Ubiquitous Computing: Providing for the Elderly (WUAUC) · doi:10.1145/564526.564539 · Cited by 15

Music Clustering With Features From Different Information Sources

Li, T., Ogihara, M., Peng, W., Shao, B., & Shenghuo Zhu, B. (2009) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2009.2012942 · Cited by 14

Multimodal Fusion of Smart Home and Text-based Behavior Markers for Clinical Assessment Prediction

Sprint, G., Cook, D. J., Schmitter‐Edgecombe, M., & Holder, L. B. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3531231 · Cited by 13

Natural interaction with a virtual guide in a virtual environment

Hofs, D. H. W., Theune, M., & den Akker, R. O. (2009) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-009-0024-6 · Cited by 13

Joint interpretation of input speech and pen gestures for multimodal human-computer interaction

Hui, P. Y., & Meng, H. M. L. (2006) · Interspeech (USB) · doi:10.21437/interspeech.2006-362 · Cited by 13

Mind: A Context-Based Multimodal Interpretation Framework in Conversational Systems

Chai, J. Y., Pan, S., & Zhou, M. X. (2005) · Text, speech and language technology · doi:10.1007/1-4020-3933-6_12 · Cited by 13

A visual modality for the augmentation of paper

McGee, D. R., Misha Pavel, C., Adami, A. M., Wang, G., & Cohen, P. R. (2001) · Workshop on Perceptive User Interfaces · doi:10.1145/971478.971480 · Cited by 13

Gaze-contingent automatic speech recognition

Cooke, N. J., & Russell, M. J. (2008) · IET Signal Processing · doi:10.1049/iet-spr:20070127 · Cited by 12

A Personal Mobility Assistant based on Ambient Intelligence to Promote Sustainable Travel Choices

Magliocchetti, D., Gielow, M., Vigili, F. D., Conti, G., & Amicis, R. D. (2011) · Procedia Computer Science · doi:10.1016/j.procs.2011.07.124 · Cited by 12

Human gait pattern changes detection system: A multimodal vision-based and novelty detection learning approach

Paulo, J., Asvadi, A., Peixoto, P., & Amorim, P. (2017) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2017.08.002 · Cited by 12

Late fusion of individual engines for improved recognition of negative emotion in speech - learning vs. democratic vote

Schuller, B. W., Metze, F., Steidl, S., Batliner, A. M., Eyben, F., & Polzehl, T. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5494986 · Cited by 12

Multimodal Integration (Image and Text) Using Ontology Alignment

Abu-Shareha, A. A., Rajeswari, M., & Ramachandram, D. (2009) · American Journal of Applied Sciences · doi:10.3844/ajassp.2009.1217.1224 · Cited by 12

Context Shifts: Extending the Meanings of Physical Objects With Language

McGee, D. R., Misha Pavel, C., & Cohen, P. R. (2001) · Human-Computer Interaction · doi:10.1207/s15327051hci16234_15 · Cited by 11

Semi-Synchronous Speech and Pen Input

Watanabe, Y., Iwata, K., Nakagawa, R., Shinoda, K., & Furui, S. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.366936 · Cited by 11

Human Emotion Recognition from Body Language of the Head using Soft Computing Techniques

Zhao, Y. (2012) · uO Research (University of Ottawa) · doi:10.20381/ruor-6160 · Cited by 11

Clustering genes using gene expression and text literature data

Yang, C., Zeng, E., Li, T., & Narasimhan, G. (2005) · IEEE Computational Systems Bioinformatics Conference (CSB) · doi:10.1109/csb.2005.23 · Cited by 10

Statistical multimodal integration for intelligent HCI

Wu, L., Oviatt, S. L., & Cohen, P. R. (2003) · Machine learning for signal processing ... · doi:10.1109/nnsp.1999.788168 · Cited by 8

Unification-based multimodal integration

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Breaking the Robustness Barrier: Recent Progress on the Design of Robust Multimodal Systems

Oviatt, S. L. (2002) · Advances in computers · doi:10.1016/s0065-2458(02)80009-2 · Cited by 62

A contextual multimodal integrator

Boda, P. (2006) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1180995.1181022 · Cited by 5

A framework for multimodal integration

Gupta, A. (2022) · UNSWorks (University of New South Wales, Sydney, Australia) · doi:10.26190/unsworks/8548