Yu Chen¹, Panpan Chen¹, Jun Wu^1,2,3, Shuai Guo¹, Jiahui Huang¹, Xinyi Zhu¹, Qun Zhang^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087279 - 15 September 2026

Abstract Multimodal Emotion Recognition in Conversations (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they apply shared or coarse-grained graph construction rules across modalities, overlooking their distinct dependency patterns; (2) they rely on fixed-activation MLPs for feature transformation, limiting nonlinear representation capacity in complex emotional scenarios; (3) they focus predominantly on contextual modeling while underexploring local emotion discrimination between related utterances. To address these issues, we propose Joint Differentiated Modality-Aware Heterogeneous Graph and Local Emotion Difference Supervision for Multimodal Emotion Recognition in Conversations (DMHG-LEDS), a novel MERC framework. Specifically, modality-aware… More >

Junjie Wu, Yang Liu, Danyi Sheng, Shiwei Cheng^*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085839 - 15 September 2026

Abstract With the development of brain-computer interfaces (BCI), more and more studies are using electroencephalography (EEG) for emotion recognition. Traditional emotion recognition often uses 2D videos and pictures to stimulate emotions, which do not provide an immersive feeling. Virtual reality (VR) can provide a more immersive and realistic experience, and recent studies are beginning to utilize EEG for emotion recognition in VR. However, due to the limited information on single-modal features, it is not possible to fully recognize individual emotions. To address this problem, we proposed a multi-modal approach in VR, which utilized a VR scene… More >

Reon Yoshida¹, Keiko Ono^2,*, Kentaro Ohki³, Takuya Futagami²

Journal of Quantum Computing, Vol.8, pp. 123-144, 2026, DOI:10.32604/jqc.2026.086882 - 07 September 2026

Abstract Quantum machine learning (QML) has attracted significant attention for its potential to accelerate computation and improve efficiency, particularly through quantum feature maps that may enable the separation of data not linearly separable in classical spaces. Although this capability remains largely theoretical, it represents a promising direction for addressing complex learning tasks. However, current quantum devices suffer from low error tolerance and a limited number of qubits, which has spurred interest in hybrid quantum–classical approaches. One such application is EEG-based emotion recognition, which involves complex, nonlinear signals and substantial inter-subject variability. While prior studies have applied… More >

Muhammad Sheraz¹, Adil Majeed¹, Shehzad Khalid^2,3,*, Yazeed Alkhrijah^4,*, Sulieman S. Alshuhri⁵, Hasan Mujtaba¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.086256 - 28 August 2026

Abstract Emotion recognition plays a crucial role in enabling intelligent human–computer interaction, yet research in low-resource languages such as Urdu remains limited, particularly in multimodal settings. This study proposes a multimodal deep learning framework for Urdu emotion recognition by integrating speech and text modalities. The approach leverages transformer-based models, namely wav2vec 2.0 for audio representation and MuRIL for text representation, combined using a late fusion strategy for classification. Experiments were conducted on the UMED dataset, consisting of 8269 multimodal instances across five emotion classes. The proposed multimodal model achieved an accuracy of 0.701 and an F1-score More >

Eman Attallah H. Aljabarti, Mohd Yamani Idna Idris^*, Ainuddin Wahid Abdul Wahab

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083479 - 28 August 2026

Abstract Facial emotion recognition (FER) aims to recognize and classify human emotional expressions accurately. Although there has been significant progress in developing FER models with respectable accuracy, the accuracy still has substantial room for improvement. These claims are supported by several factors, including poor parameter tuning, class imbalance, dataset bias, generalization limitations, and inefficient preprocessing. These factors make it more difficult to capture hierarchical and high-level features in training data. To address these limitations, therefore, this work develops and fine-tunes a deep convolutional neural network-based model to effectively learn discriminative facial features. First, the data are… More > Graphic Abstract

David Hason Rudd^1,*, Cesar Sanin², Md Rafiqul Islam³, Xianzhi Wang¹, Huan Huo¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084015 - 13 August 2026

Abstract The accurate classification of human emotions from speech remains a formidable challenge due to the dynamic, non-stationary properties of audio signals and pervasive background noise. Traditional single-domain extraction methods frequently fail to capture overlapping acoustic phenomena, resulting in high misclassification rates among acoustically similar emotions. To overcome this, we propose the Dual-Decomposition Ensemble (DDE-SER), an architecture that synergizes 1D adaptive frequency filtering with 2D spatial spectrogram separation. The framework operates through two distinct pipelines: an adaptive time-domain branch that leverages VGG-optiVMD to autonomously extract Intrinsic Mode Functions (IMFs), and a structural spectrogram branch that applies… More >

Jorge Gómez-Navalón, Ronghao Pan, Tomas Bernal-Beltrán, José Antonio García-Díaz^*, Rafael Valencia-García

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085437 - 27 July 2026

Abstract Traditionally, speech emotion recognition has relied on supervised models that require task-specific training and annotated data. However, the recent emergence of audio-language models introduces a more flexible paradigm that enables multimodal reasoning through speech and natural language interaction. Nevertheless, their effectiveness for emotion recognition remains unclear. In this study, we evaluate audio-language models for speech emotion classification using the Spanish MEACorpus dataset and compare three approaches: prompt-based inference, embedding-based classification with lightweight classifiers, and instruction-tuned models with parameter-efficient fine-tuning plus a hybrid architecture based on class-specific confidence-driven routing. Our results show that the hybrid approach… More >

Shing-Tai Pan^*, Yi-Zhen Huang, Zhi-Qing Chen

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081460 - 23 July 2026

Abstract This paper proposes a multimodal fusion framework that integrates speech and visual features to enhance the accuracy of emotion recognition. The principal contribution lies in extending the visual component from single-image to multi-image emotion recognition. Specifically, the proposed framework employs an InceptionV3 Convolutional Neural Network (CNN)-based architecture to extract features from multiple facial images representing the speaker’s expressions throughout an utterance. These features are concatenated into a single vector and subsequently processed by Long Short-Term Memory (LSTM) or Hidden Markov Model (HMM) for temporal modeling. For the speech modality, Mel-Frequency Cepstral Coefficients (MFCC) or filter… More >

Rashid Jahangir^1,*, Muhammad Asif Nauman², Oumaima Saidani³, Faisal Ramzan²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080025 - 23 July 2026

Abstract Speech Emotion Recognition (SER) is a critical component of affective computing with broad applications in human–computer interaction, mental health monitoring, and intelligent multimedia systems. However, SER remains challenging due to the emotional ambiguity, lack of labeled data, class imbalance, and speaker variability. This study presents an effective SER framework that integrates contrastive representation learning, optimized spectrogram-based data augmentation, and selective synthetic data generation by using TimeGAN to enhance emotion classification performance. Contrastive learning enables the model to better discriminate acoustically similar emotions while Optuna automatically tunes augmentation strategies such as noise injection, time shifting, and More >

Mohammed Shukur Alfaras^1,2,*, Oguz Karan³, Sefer Kurnaz¹, Ayca Kurnaz Turkben⁴

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079577 - 15 June 2026

Abstract Despite deep learning’s high precision in emotion identification, centralized training is associated with privacy and scalability concerns. The privacy-preserving federated learning model, Federated Hybrid-Optimized Emotion Recognition (Fed-HOER), introduced in this paper is an auto-tuning hyperparameters optimizer based on a hybrid Dung Beetle Optimizer-Fick’s Law Algorithm (DBO-FLA) optimizer. The global and local searches are optimized at two levels, and validation loss is minimized by 22%–24% without sharing raw data. The experiments on Extended Cohn–Kanade (CK+), Japanese Female Facial Expressions (JAFFE), and Karolinska Directed Emotional Faces (KDEF) exhibit a high generalization rate with a mean accuracy of More >