Emotion recognition from unimodal to multimodal analysis: A review

被引：63

作者：

Ezzameli, K. ^{[1
]}

Mahersia, H. ^{[1
]}

机构：

[1] Univ Carthage, Fac Sci Bizerte, Data Engn & Applicat Lab, Artificial Intelligence, Zarzouna 7021, Tunisia

来源：

INFORMATION FUSION | 2023年 / 99卷

关键词：

Affective computing; Deep learning; Emotion recognition; Fusion; Modality; Multimodality; FACIAL EXPRESSION RECOGNITION; SENTIMENT ANALYSIS; AUTOMATIC-ANALYSIS; SPEECH; DATABASE; STATE; FACE; CLASSIFICATION; AUTOENCODER; FRAMEWORK;

D O I：

10.1016/j.inffus.2023.101847

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The omnipresence of numerous information sources in our daily life brings up new alternatives for emotion recognition in several domains including e-health, e-learning, robotics, and e-commerce. Due to the variety of data, the research area of multimodal machine learning poses special problems for computer scientists; how did the field of emotion recognition progress in each modality and what are the most common strategies for recognizing emotions? What part does deep learning play in this? What is multimodality? How did it progress? What are the methods of information fusion? What are the most used datasets in each modality and in multimodal recognition? We can understand and compare the various methods by answering these questions.

引用

页数：30

共 259 条

[1] Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models [J].

Abbaschian, Babak Joze ;

Sierra-Sosa, Daniel ;

Elmaghraby, Adel .

SENSORS, 2021, 21 (04) :1-27

[2] FEEL: a French Expanded Emotion Lexicon [J].

Abdaoui, Amine ;

Aze, Jerome ;

Bringay, Sandra ;

Poncelet, Pascal .

LANGUAGE RESOURCES AND EVALUATION, 2017, 51 (03) :833-855

[3] Robust Speech Emotion Recognition Using CNN plus LSTM Based on Stochastic Fractal Search Optimization Algorithm [J].

Abdelhamid, Abdel Aziza ;

El-Kenawy, El-Sayed M. ;

Alotaibi, Bandar ;

Amer, Ghadam ;

Abdelkader, Mahmoud Y. ;

Ibrahim, Abdelhameed ;

Eid, Marwa Metwally .

IEEE ACCESS, 2022, 10 :49265-49284

[4] EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks [J].

Abdul-Mageed, Muhammad ;

Ungar, Lyle .

PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, 2017, :718-728

[5] Transformer models for text-based emotion detection: a review of BERT-based approaches [J].

Acheampong, Francisca Adoma ;

Nunoo-Mensah, Henry ;

Chen, Wenyu .

ARTIFICIAL INTELLIGENCE REVIEW, 2021, 54 (08) :5789-5829

[6] Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning [J].

Aggarwal, Apeksha ;

Srivastava, Akshat ;

Agarwal, Ajay ;

Chahal, Nidhi ;

Singh, Dilbag ;

Alnuaim, Abeer Ali ;

Alhadlaq, Aseel ;

Lee, Heung-No .

SENSORS, 2022, 22 (06)

[7] Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy [J].

Agrawal, Abhinav ;

Mittal, Namita .

VISUAL COMPUTER, 2020, 36 (02) :405-412

[8] Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers [J].

Akcay, Mehmet Berkehan ;

Oguz, Kaya .

SPEECH COMMUNICATION, 2020, 116 :56-76

[9] How Intense Are You? Predicting Intensities of Emotions and Sentiments using Stacked Ensemble [J].

Akhtar, Md Shad ;

Ekbal, Asif ;

Cambria, Erik .

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, 2020, 15 (01) :64-75

[10]

Al-Dujaili M.J., 2023, WIRELESS PERS COMMUN, P1

← 1 2 3 4 5 6 7 8 9 10 →