{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/emotion-recognition/papers/12","list_of":"/task/emotion-recognition","task":"Emotion Recognition","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":12,"pages_in_order":21,"rows_per_page":100,"rows":[1101,1200],"of":2041,"counts":{"archive_papers_tagged":2041,"with_a_code_link":614,"where_syntology_ran_a_sample":69,"not_listed_spam_title":0,"listed":2041,"listed_where_code_ran":69,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":60,"every_run_a_failure_of_syntologys_instrument":9,"listed_with_a_run_with_no_instrument_failure":60,"listed_every_run_a_failure_of_syntologys_instrument":9,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/emotion-recognition","prev":"/task/emotion-recognition/papers/11","next":"/task/emotion-recognition/papers/13","papers":[{"url":null,"slug":"emoset-a-large-scale-visual-emotion-dataset","title":"EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes","date":"2023-07-16","arxiv_id":"2307.07961","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotion-recognition-based-on-multi-modal","title":"Emotion recognition based on multi-modal electrophysiology multi-head attention Contrastive Learning","date":"2023-07-12","arxiv_id":"2308.01919","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotion-analysis-on-eeg-signal-using-machine","title":"Emotion Analysis on EEG Signal Using Machine Learning and Neural Network","date":"2023-07-09","arxiv_id":"2307.05375","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-end-to-end-spatio-temporal-attention","title":"A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition","date":"2023-07-06","arxiv_id":"2307.03068","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-raw-waveforms-with-deep-learning","title":"Evaluating raw waveforms with deep learning frameworks for speech emotion recognition","date":"2023-07-06","arxiv_id":"2307.02820","repositories_listed":0,"syntology":null},{"url":null,"slug":"selinet-sentiment-enriched-lightweight","title":"SeLiNet: Sentiment enriched Lightweight Network for Emotion Recognition in Images","date":"2023-07-06","arxiv_id":"2307.02773","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-emotion-recognition-based-on-galvanic","title":"Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM","date":"2023-07-04","arxiv_id":"2307.05383","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dual-stream-recurrence-attention-network","title":"A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog","date":"2023-07-02","arxiv_id":"2307.00449","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-interpretation-of-the-relationship","title":"Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition","date":"2023-06-30","arxiv_id":"2306.17500","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotion-analysis-of-tweets-banning-education","title":"Emotion Analysis of Tweets Banning Education in Afghanistan","date":"2023-06-28","arxiv_id":"2306.16268","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-language-speech-emotion-recognition","title":"Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers","date":"2023-06-23","arxiv_id":"2306.13804","repositories_listed":0,"syntology":null},{"url":null,"slug":"tacoformer-token-channel-compounded-cross","title":"TACOformer:Token-channel compounded Cross Attention for Multimodal Emotion Recognition","date":"2023-06-23","arxiv_id":"2306.13592","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-time-based-models-for","title":"A Comparison of Time-based Models for Multimodal Emotion Recognition","date":"2023-06-22","arxiv_id":"2306.13076","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-low-rank-matching-attention-based-cross","title":"A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition","date":"2023-06-16","arxiv_id":"2306.17799","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedmultimodal-a-benchmark-for-multimodal","title":"FedMultimodal: A Benchmark For Multimodal Federated Learning","date":"2023-06-15","arxiv_id":"2306.09486","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-learning-based-novelty-aware","title":"Continuous Learning Based Novelty Aware Emotion Recognition System","date":"2023-06-14","arxiv_id":"2306.08733","repositories_listed":0,"syntology":null},{"url":null,"slug":"emersk-explainable-multimodal-emotion","title":"EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge","date":"2023-06-14","arxiv_id":"2306.08657","repositories_listed":0,"syntology":null},{"url":null,"slug":"safer-situation-aware-facial-emotion","title":"SAFER: Situation Aware Facial Emotion Recognition","date":"2023-06-14","arxiv_id":"2306.09372","repositories_listed":0,"syntology":null},{"url":null,"slug":"gemo-clap-gender-attribute-enhanced","title":"GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition","date":"2023-06-13","arxiv_id":"2306.07848","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-attention-mechanisms-for-multimodal","title":"Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus","date":"2023-06-12","arxiv_id":"2306.07115","repositories_listed":0,"syntology":null},{"url":null,"slug":"mfas-emotion-recognition-through-multiple","title":"MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition","date":"2023-06-12","arxiv_id":"2306.09361","repositories_listed":0,"syntology":null},{"url":null,"slug":"modality-influence-in-multimodal-machine","title":"Modality Influence in Multimodal Machine Learning","date":"2023-06-10","arxiv_id":"2306.06476","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-emotional-representations-from","title":"Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech","date":"2023-06-09","arxiv_id":"2306.05709","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantum-probability-driven-framework-for","title":"A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysis","date":"2023-06-06","arxiv_id":"2306.03650","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-multimodal-emotion-recognition-1","title":"Interpretable Multimodal Emotion Recognition using Facial Features and Physiological Signals","date":"2023-06-05","arxiv_id":"2306.02845","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthesizing-affective-neurophysiological","title":"Synthesizing Affective Neurophysiological Signals Using Generative Models: A Review Paper","date":"2023-06-05","arxiv_id":"2306.03112","repositories_listed":0,"syntology":null},{"url":null,"slug":"ldeb-label-digitization-with-emotion","title":"LDEB -- Label Digitization with Emotion Binarization and Machine Learning for Emotion Recognition in Conversational Dialogues","date":"2023-06-03","arxiv_id":"2306.02193","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-semantic-information-for-efficient","title":"Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models","date":"2023-05-30","arxiv_id":"2305.19184","repositories_listed":0,"syntology":null},{"url":null,"slug":"transforming-the-embeddings-a-lightweight","title":"Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks","date":"2023-05-29","arxiv_id":"2305.18640","repositories_listed":0,"syntology":null},{"url":null,"slug":"arpanemo-an-open-source-dataset-for-fine","title":"ArPanEmo: An Open-Source Dataset for Fine-Grained Emotion Recognition in Arabic Online Content during COVID-19 Pandemic","date":"2023-05-27","arxiv_id":"2305.17580","repositories_listed":0,"syntology":null},{"url":null,"slug":"inter-subject-emotion-recognition-using","title":"Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal","date":"2023-05-27","arxiv_id":"2305.19379","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotion-experiencer-recognition-as-a","title":"Automatic Emotion Experiencer Recognition","date":"2023-05-26","arxiv_id":"2305.16731","repositories_listed":0,"syntology":null},{"url":null,"slug":"asr-and-emotional-speech-a-word-level","title":"ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition","date":"2023-05-25","arxiv_id":"2305.16065","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-study-of-pre-trained-bert-models","title":"Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data","date":"2023-05-25","arxiv_id":"2305.15722","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-personality-perception","title":"Transfer Learning for Personality Perception via Speech Emotion Recognition","date":"2023-05-25","arxiv_id":"2305.16076","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-efficacy-and-noise-robustness-of","title":"On the Efficacy and Noise-Robustness of Jointly Learned Speech Emotion and Automatic Speech Recognition","date":"2023-05-21","arxiv_id":"2305.12540","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-representations-in-speech","title":"Self-supervised representations in speech-based depression detection","date":"2023-05-20","arxiv_id":"2305.12263","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-aware-mixed-attention-based","title":"Temporal Aware Mixed Attention-based Convolution and Transformer Network (MACTN) for EEG Emotion Recognition","date":"2023-05-18","arxiv_id":"2305.18234","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotion-recognition-based-on-psychological","title":"Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation","date":"2023-05-15","arxiv_id":"2305.10446","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-lightweight-domain-adversarial-neural","title":"A Lightweight Domain Adversarial Neural Network Based on Knowledge Distillation for EEG-based Cross-subject Emotion Recognition","date":"2023-05-12","arxiv_id":"2305.07446","repositories_listed":0,"syntology":null},{"url":"/paper/versatile-audio-visual-learning-for-handling","slug":"versatile-audio-visual-learning-for-handling","title":"Versatile audio-visual learning for emotion recognition","date":"2023-05-12","arxiv_id":"2305.07216","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotion-recognition-for-challenged-people","title":"Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network","date":"2023-05-11","arxiv_id":"2305.06842","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-exploration-into-the-performance-of","title":"An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for \"In the Wild'' Edge Applications","date":"2023-05-09","arxiv_id":"2305.05443","repositories_listed":0,"syntology":null},{"url":null,"slug":"cit-emotionnet-cnn-interactive-transformer","title":"CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition","date":"2023-05-07","arxiv_id":"2305.05548","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-vector-quantized-masked-autoencoder-for-1","title":"A vector quantized masked autoencoder for audiovisual speech emotion recognition","date":"2023-05-05","arxiv_id":"2305.03568","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-level-context-representation-for-emotion","title":"High-Level Context Representation for Emotion Recognition in Images","date":"2023-05-05","arxiv_id":"2305.03500","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-resistant-multimodal-transformer-for","title":"Noise-Resistant Multimodal Transformer for Emotion Recognition","date":"2023-05-04","arxiv_id":"2305.02814","repositories_listed":0,"syntology":null},{"url":null,"slug":"si-lstm-speaker-hybrid-long-short-term-memory","title":"SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation","date":"2023-05-04","arxiv_id":"2305.03506","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-deep-learning-techniques-for-3","title":"A Review of Deep Learning Techniques for Speech Processing","date":"2023-04-30","arxiv_id":"2305.00359","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-pre-trained-speech-and","title":"A Comparative Study of Pre-trained Speech and Audio Embeddings for Speech Emotion Recognition","date":"2023-04-22","arxiv_id":"2304.11472","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-speaker-anonymization-on","title":"Evaluation of Speaker Anonymization on Emotional Speech","date":"2023-04-15","arxiv_id":"2305.01759","repositories_listed":0,"syntology":null},{"url":"/paper/hcam-hierarchical-cross-attention-model-for","slug":"hcam-hierarchical-cross-attention-model-for","title":"HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition","date":"2023-04-14","arxiv_id":"2304.06910","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-and-improvement-for-speech","title":"An Empirical Study and Improvement for Speech Emotion Recognition","date":"2023-04-08","arxiv_id":"2304.03899","repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-and-evaluating-speech-emotion","title":"Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP","date":"2023-04-03","arxiv_id":"2304.00860","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-experimental-study-in-real-time-facial","title":"An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset","date":"2023-04-02","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-self-supervised-multimodal","title":"Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition","date":"2023-03-29","arxiv_id":"2303.17611","repositories_listed":0,"syntology":null},{"url":null,"slug":"depression-detection-in-social-media-posts","title":"Depression detection in social media posts using affective and social norm features","date":"2023-03-24","arxiv_id":"2303.14279","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-n-gru-end-to-end-speech-emotion","title":"CNN-n-GRU: end-to-end speech emotion recognition from raw waveform signal using CNNs and gated recurrent unit networks","date":"2023-03-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-neural-architecture-search-for","title":"Efficient Neural Architecture Search for Emotion Recognition","date":"2023-03-23","arxiv_id":"2303.13653","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-people-respond-to-the-covid-19-pandemic","title":"How People Respond to the COVID-19 Pandemic on Twitter: A Comparative Analysis of Emotional Expressions from US and India","date":"2023-03-19","arxiv_id":"2303.10560","repositories_listed":0,"syntology":null},{"url":null,"slug":"tollywood-emotions-annotation-of-valence","title":"Tollywood Emotions: Annotation of Valence-Arousal in Telugu Song Lyrics","date":"2023-03-16","arxiv_id":"2303.09364","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-large-scale-unlabeled-faces-to","title":"Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition","date":"2023-03-15","arxiv_id":"2303.08617","repositories_listed":0,"syntology":null},{"url":null,"slug":"reevaluating-data-partitioning-for-emotion","title":"Reevaluating Data Partitioning for Emotion Detection in EmoWOZ","date":"2023-03-15","arxiv_id":"2303.13364","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-eeg-based-emotion-recognition-by","title":"Improving EEG-based Emotion Recognition by Fusing Time-frequency And Spatial Representations","date":"2023-03-14","arxiv_id":"2303.11421","repositories_listed":0,"syntology":null},{"url":"/paper/coordvit-a-novel-method-of-improve-vision","slug":"coordvit-a-novel-method-of-improve-vision","title":"CoordViT: A Novel Method of Improve Vision Transformer-Based Speech Emotion Recognition using Coordinate Information Concatenate","date":"2023-03-10","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-based-neural-decoding","title":"A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfaces","date":"2023-03-08","arxiv_id":"2303.04391","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-trained-model-representations-and-their","title":"Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis","date":"2023-03-03","arxiv_id":"2303.03177","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-auxiliary-tasks-in-multimodal-fusion-of","title":"Using Auxiliary Tasks In Multimodal Fusion Of Wav2vec 2.0 And BERT For Multimodal Emotion Recognition","date":"2023-02-27","arxiv_id":"2302.13661","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-knowledge-distillation-of-self","title":"Ensemble knowledge distillation of self-supervised speech models","date":"2023-02-24","arxiv_id":"2302.12757","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-aware-bayesian-co-attention-for","title":"Knowledge-aware Bayesian Co-attention for Multimodal Emotion Recognition","date":"2023-02-20","arxiv_id":"2302.09856","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-implicit-distribution-alignment-networks","title":"Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition","date":"2023-02-17","arxiv_id":"2302.08921","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-smoothed-imbalance-data-improves","title":"Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition","date":"2023-02-17","arxiv_id":"2302.08650","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-large-scale-analysis-of-persian-tweets","title":"A Large-Scale Analysis of Persian Tweets Regarding Covid-19 Vaccination","date":"2023-02-09","arxiv_id":"2302.04511","repositories_listed":0,"syntology":null},{"url":null,"slug":"audio-representation-learning-by-distilling","title":"Audio Representation Learning by Distilling Video as Privileged Information","date":"2023-02-06","arxiv_id":"2302.02845","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-fusion-techniques-for-utterance","title":"cross-modal fusion techniques for utterance-level emotion recognition from text and speech","date":"2023-02-05","arxiv_id":"2302.02447","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-of-segment-level-feature","title":"deep learning of segment-level feature representation for speech emotion recognition in conversations","date":"2023-02-05","arxiv_id":"2302.02419","repositories_listed":0,"syntology":null},{"url":null,"slug":"csat-ftcn-a-fuzzy-oriented-model-with","title":"CSAT‑FTCN: A Fuzzy‑Oriented Model with Contextual Self‑attention Network for Multimodal Emotion Recognition","date":"2023-01-31","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"facial-emotion-recognition","title":"Facial Expression Recognition using Squeeze and Excitation-powered Swin Transformers","date":"2023-01-26","arxiv_id":"2301.10906","repositories_listed":0,"syntology":null},{"url":null,"slug":"bert-erc-fine-tuning-bert-is-enough-for","title":"BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation","date":"2023-01-17","arxiv_id":"2301.06745","repositories_listed":0,"syntology":null},{"url":null,"slug":"modulation-spectral-features-for-speech","title":"Modulation spectral features for speech emotion recognition using deep neural networks","date":"2023-01-14","arxiv_id":"2301.05868","repositories_listed":0,"syntology":null},{"url":null,"slug":"litelstm-architecture-based-on-weights","title":"LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks","date":"2023-01-12","arxiv_id":"2301.04794","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotion-recognition-from-microblog-managing","title":"Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN","date":"2023-01-08","arxiv_id":"2301.02971","repositories_listed":0,"syntology":null},{"url":null,"slug":"seamless-multimodal-biometrics-for-continuous","title":"Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring","date":"2023-01-08","arxiv_id":"2301.03045","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-exploitative-and-explorative-gwo-svm","title":"A Novel Exploitative and Explorative GWO-SVM Algorithm for Smart Emotion Recognition","date":"2023-01-05","arxiv_id":"2301.01887","repositories_listed":0,"syntology":null},{"url":"/paper/multi-label-compound-expression-recognition-c","slug":"multi-label-compound-expression-recognition-c","title":"Multi-Label Compound Expression Recognition: C-EXPR Database & Network","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-approaches-for-optimising","title":"Feature Selection Approaches for Optimising Music Emotion Recognition Methods","date":"2022-12-27","arxiv_id":"2212.13369","repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-at-the-tail","title":"Quality at the Tail of Machine Learning Inference","date":"2022-12-25","arxiv_id":"2212.13925","repositories_listed":0,"syntology":null},{"url":null,"slug":"amdet-attention-based-multiple-dimensions-eeg","title":"AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition","date":"2022-12-23","arxiv_id":"2212.12134","repositories_listed":0,"syntology":null},{"url":null,"slug":"emotion-recognition-with-pre-trained","title":"Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals","date":"2022-12-22","arxiv_id":"2212.13885","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-emotion-recognition-among-couples","title":"Multimodal Emotion Recognition among Couples from Lab Settings to Daily Life using Smartwatches","date":"2022-12-21","arxiv_id":"2212.13917","repositories_listed":0,"syntology":null},{"url":null,"slug":"intermulti-multi-view-multimodal-interactions","title":"InterMulti:Multi-view Multimodal Interactions with Text-dominated Hierarchical High-order Fusion for Emotion Analysis","date":"2022-12-20","arxiv_id":"2212.10030","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-generalizability-of-text-based","title":"Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features","date":"2022-12-19","arxiv_id":"2212.09465","repositories_listed":0,"syntology":null},{"url":null,"slug":"effmulti-efficiently-modeling-complex","title":"EffMulti: Efficiently Modeling Complex Multimodal Interactions for Emotion Analysis","date":"2022-12-16","arxiv_id":"2212.08661","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-strategies-to-improve","title":"Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods","date":"2022-12-16","arxiv_id":"2212.08744","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangling-prosody-representations-with","title":"Disentangling Prosody Representations with Unsupervised Speech Reconstruction","date":"2022-12-14","arxiv_id":"2212.06972","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-approach-for-improving-automatic-mouth","title":"An Approach for Improving Automatic Mouth Emotion Recognition","date":"2022-12-12","arxiv_id":"2212.06009","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-emotion-recognition","title":"A comparative study of emotion recognition methods using facial expressions","date":"2022-12-05","arxiv_id":"2212.03102","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-smart-classroom-concept","title":"A survey of smart classroom: Concept, technologies and facial emotions recognition application","date":"2022-12-03","arxiv_id":"2212.01675","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuse-and-adapt-investigating-the-use-of-pre","title":"Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems","date":"2022-12-02","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-constant-q-filterbank-based","title":"Analysis of constant-Q filterbank based representations for speech emotion recognition","date":"2022-11-29","arxiv_id":"2211.16363","repositories_listed":0,"syntology":null}],"record_sha256":"eacafe9e0845d47c1023f33a14faf8a78af2c3e710c8924e2e1f2d8bd5e68412","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}