[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125573-en":3,"doc-seo-125573-105":30,"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125573,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Emotional Expression Detection in Spoken Language Employing Machine Learning Algorithms - Research Paper","Emotional Expression Detection in Spoken Language Employing Machine Learning Algorithms addresses speech emotion recognition by modeling vocal characteristics such as pitch, timbre, loudness, and vocal tone. The study targets emotions including anger, sadness, fear, neutrality, disgust, pleasant surprise, and happiness using signal decomposition with EMD and feature extraction with spectral descriptors, MFCC, GTCC, centroid, rolloff, entropy, and harmonic/energy measures. Experiments on CREMA-D and TESS datasets train ML classifiers including SVM, Neural Network, Ensemble, and KNN, reporting notable test and training accuracies.","Emotional Expression Detection in Spoken Language Employing Machine Learning Algorithms  \nMehrab Hosain 1, Most. Yeasmin Arafat2, Gazi Zahirul Islam3, Jia Uddin4, Md. Mobarak Hossain5, and  \nFatema Alam6  \n1Department of Computer Science and Engineering, University of Information Technology & Sciences, Dhaka-1212,  \nBangladesh  \nemail: [robinhosain@gmail.com](robinhosain@gmail.com)  \n2Department of Computer Science and Engineering, University of Information Technology & Sciences, Dhaka-1212,  \nBangladesh  \n[email: yearashefa@gmail.com](email: yearashefa@gmail.com)  \n3Department of Computer Science and Engineering, Southeast University, Dhaka-1208, Bangladesh  \nemail: [gazi.islam@seu.edu.bd](gazi.islam@seu.edu.bd)  \n4AI and Big Data Department, Endicott College, Woosong University, Daejeon, Korea,  \nemail: [jia.uddin@wsu.ac.kr](jia.uddin@wsu.ac.kr)  \n5Department of Computer Science and Engineering, Daffodil International University, Dhaka-1216, Bangladesh  \n[mobarak15-9636@diu.edu.bd](mobarak15-9636@diu.edu.bd)  \n6Department of Physics, Jahangirnagar University, Dhaka-1342, Bangladesh  \nemail: [fatemazinnah89@gmail.com](fatemazinnah89@gmail.com)  \nAbstract:  \nThere are a variety of features of the human voice that can be classified as pitch, timbre, loudness, and vocal tone. It is observed in numerous incidents that human expresses their feelings using different vocal qualities when they are speaking. The primary objective of this research is to recognize different emotions of human beings such as anger, sadness, fear, neutrality, disgust, pleasant surprise, and happiness by using several MATLAB functions namely, spectral descriptors, periodicity, and harmonicity. To accomplish the work, we analyze the CREMA-D (Crowd-sourced Emotional Multimodal Actors Data) & TESS (Toronto Emotional Speech Set) datasets of human speech. The audio file contains data that have various characteristics (e.g., noisy, speedy, slow) thereby the efficiency of the ML (Machine Learning) models increases significantly. The EMD (Empirical Mode Decomposition) is utilized for the process of signal decomposition. Then, the features are extracted through the use of several techniques such as the MFCC, GTCC, spectral centroid, rolloff point, entropy, spread, flux, harmonic ratio, energy, skewness, flatness, and audio delta. The data is trained using some renowned ML models namely, Support Vector Machine, Neural Network, Ensemble, and KNN. The algorithms show an accuracy of 67.7%, 63.3%, 61.6%, and 59.0% respectively for the test data and 77.7%, 76.1%, 99.1%, and 61.2% for the training data. We have conducted experiments using Matlab and the result shows that our model is very prominent and flexible than existing similar works.  \nKeywords:  \nEmotional Expression, Empirical Mode Decomposition, Machine Learning Algorithms, Speech Data Sets  \n1. INTRODUCTION  \nThe human voice is very complex and can show a wide range of emotions. Emotion in speech gives information about how people act or feel. There are many functions of the human vocal system that make it possible for humans to speak. These include tone, pitch, energy, entropy , and many other aspects of the  \nspeech. The increasing need for human-machine interactions indicates that more tasks to be done to improve the results of these interactions, like giving computer and machine interfaces the ability to understand how a person feels when they speak. Emotions play a big part in how people talk to each other. People and machines should be able to work together more effectively if computers have built-in skills for figuring out how people feel [2], [5] . Today, a lot of resources and time is being spent on improving artificial intelligence and smart machines to make our life easier and more comfortable. According to the findings of several pieces of literature, human feelings regulate the decision-making process to some extent [1]-[4] . If the machine can figure out how people are feeling when they speak, it wi","cbCaibdhobAKvrlA","https://ap.wps.com/l/cbCaibdhobAKvrlA","pdf",713030,1,15,"English","en",105,"# Introduction\n# Related Works","[{\"question\":\"Which emotions does the research aim to recognize from speech?\",\"answer\":\"The research recognizes anger, sadness, fear, neutrality, disgust, pleasant surprise, and happiness based on spoken audio characteristics.\"},{\"question\":\"What datasets and preprocessing method are used in the approach?\",\"answer\":\"The model analyzes the CREMA-D and TESS speech datasets and uses Empirical Mode Decomposition (EMD) for signal decomposition before feature extraction.\"},{\"question\":\"Which machine learning models are trained and evaluated?\",\"answer\":\"The study trains and tests Support Vector Machine, Neural Network, Ensemble, and KNN models using extracted acoustic features.\"}]","Emotional Expression Detection in Spoken Language Employing Machine Learning Algorithms - Research Paper | PDF",1785899961,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"emotional-expression-detection-in-spoken-language-employing-machine-learning-algorithms-research-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/emotional-expression-detection-in-spoken-language-employing-machine-learning-algorithms-research-paper/125573/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",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},"Which emotions does the research aim to recognize from speech?","Question",{"text":75,"@type":76},"The research recognizes anger, sadness, fear, neutrality, disgust, pleasant surprise, and happiness based on spoken audio characteristics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What datasets and preprocessing method are used in the approach?",{"text":80,"@type":76},"The model analyzes the CREMA-D and TESS speech datasets and uses Empirical Mode Decomposition (EMD) for signal decomposition before feature extraction.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are trained and evaluated?",{"text":84,"@type":76},"The study trains and tests Support Vector Machine, Neural Network, Ensemble, and KNN models using extracted acoustic features.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]