[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122890-en":3,"doc-seo-122890-105":31,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},122890,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Multimodal Emotion Recognition System Using Machine Learning Classifier - Article","Multi-modal emotion recognition identifies human emotions by integrating cues from facial expressions, voice intonation, and EEG signals. Emotion recognition is positioned for healthcare, education, and customer service, while requiring responsible handling of privacy risks. Key challenges include aligning temporal data across modalities and coping with noisy or incomplete inputs. This study addresses the alignment and robustness problem by using SVM as the machine learning classifier with IEMOCAP for speech/video and DEAP for EEG, achieving 76.22% and 68.89% accuracy, respectively.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 IssueS-8 Year 2023 Page 33-36  \nMultimodal Emotion Recognition System Using Machine Learning Classifier  \nR. Prabha1*  \n1 *Assistant Professor, Department of Computer Science, Changu Kana Thakur Arts, Commerce and Science College, New Panvel, [rashmiryan22@gmail.com](rashmiryan22@gmail.com) +91-7666980624  \n*Corresponding Author: R. Prabha  \n*Assistant Professor, Department of Computer Science, Changu Kana Thakur Arts, Commerce and Science College, New Panvel, [rashmiryan22@gmail.com](rashmiryan22@gmail.com) +91-7666980624  \n\n| CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>Multi-modal emotion recognition refers to the process of identifying human emotions using information from multiple sources, such as facial expressions, voice intonation, EEG signals etc. Ultimately, emotion recognition is poised to play a pivotal role in healthcare, education, customer service etc. As we progress, it is imperative to address privacy concerns associated with this technology in a responsible manner. Challenges in multi-modal emotion recognition include aligning data from different modalities in time, dealing with noisy or incomplete information. In this paper, we aim to address this issue by employing the SVM as our machine learning classifier. Here we use IEMOCAP for speech and video and DEAP dataset for EEG signals. After applying SVM we got 76.22 % accuracy for IEMOCAP and 68.89 % accuracy for DEAP dataset.\u003Cbr>Keywords: Emotion recognition, SVM, EEG Signals, Machine Learning, Multimodal system |\n| --- | --- |\n\nIntroduction:  \nThe significance of emotion gained prominence with the emergence of cognitive psychology in the midtwentieth century, as cognitive theories highlighted the importance of both emotion and thinking processes in our daily functioning [1] . Mental health monitoring entails the ongoing evaluation and monitoring of an emotional well-being and mental state of individual. Emotion detection methods involve the collection ofEEG signals, providing valuable insights into an individual's mental health [2] . Physiological signals like Electroencephalogram (EEG) are directly generated by the central nervous system and are closely linked to human emotions. As a result, EEG signals can objectively and promptly reflect the real-time emotional state of individuals. Through monitoring stress levels, healthcare providers can provide personalized interventionsand coping strategies, assisting individuals in more effectively managing stress. In recent years, the advancement of brain-computer interface technology has led to increased maturity in the acquisition and analysis of human EEG signals. Consequently, more researchers are employing EEG-based research methods to investigate emotion recognition [3] .  \nThe main objective is to develop advanced fusion techniques to effectively combine features from different modalities, enabling the proposed model to effectively process a variety of input signals. This endeavor not only improves the accuracy of emotion recognition systems but also pushes the field towards a more refined comprehension of human emotions in diverse contexts and applications. This emotionally intelligent device  \ncan communicate directly with individuals anytime and anywhere, thereby reducing the dependence on psychologists.  \nLiterature Review:  \nThe most important speech characteristics for determining the emotions in speech are [4] targeted feature extraction for emotion recognition by creating an unsupervised technique and carrying out tests on the IEMOCAP dataset. Classification accuracy was 78.67%; however, no feature selection algorithms were used in this study, and only one dataset was examined. According to [5] states that decision tree SVM with Fisher feature selection is constructed and applied in the Berlin speech corpus and the CASIA Chinese emotion speech corpus in order to evaluate the effectiveness of this system. The experimental results show that the a","cbCaiuvwgdUi35WH","https://ap.wps.com/l/cbCaiuvwgdUi35WH","pdf",1105265,3,1,4,"English","en",105,"# Abstract\n# Introduction\n# Literature Review\n# Proposed Method\n## Algorithm 1","[{\"question\":\"What problem does the paper address in multimodal emotion recognition?\",\"answer\":\"It tackles challenges in multimodal emotion recognition, especially time alignment across modalities and dealing with noisy or incomplete information.\"},{\"question\":\"Which datasets and modalities are used for training and evaluation?\",\"answer\":\"The paper uses IEMOCAP for speech and video, and DEAP for EEG signals.\"},{\"question\":\"How does the proposed method perform emotion classification?\",\"answer\":\"It extracts modality-specific features, fuses them into a single representation, and then applies an SVM classifier to predict emotional classes.\"}]","Multimodal Emotion Recognition System Using Machine Learning Classifier - Article | PDF",1785813523,10,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":29},"multimodal-emotion-recognition-system-using-machine-learning-classifier-article","",{"@graph":37,"@context":85},[38,53,68],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":22},"https://docshare.wps.com/document/multimodal-emotion-recognition-system-using-machine-learning-classifier-article/122890/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":42,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-09-11","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in multimodal emotion recognition?","Question",{"text":75,"@type":76},"It tackles challenges in multimodal emotion recognition, especially time alignment across modalities and dealing with noisy or incomplete information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which datasets and modalities are used for training and evaluation?",{"text":80,"@type":76},"The paper uses IEMOCAP for speech and video, and DEAP for EEG signals.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method perform emotion classification?",{"text":84,"@type":76},"It extracts modality-specific features, fuses them into a single representation, and then applies an SVM classifier to predict emotional classes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":47,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":30,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":30,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]