[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120592-en":3,"doc-seo-120592-105":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":4,"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},120592,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",6,"Technology","Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques - Abstract","Artificial intelligence and smart home technologies drive demand for speech recognition that enables intuitive interaction between users and devices. Speech recognition supports controlling home appliances and executing everyday actions via spoken commands, removing reliance on touch screens or physical interfaces. This study develops a sentence-based speech classification model for recognizing human actions in smart homes. A labeled dataset covering categories, subcategories, and actions is processed with Python machine learning and CountVectorizer features. Results achieve 82.99% category accuracy, 76.19% subcategory accuracy, and 90.28% action accuracy, confirming feasibility for natural language systems.","Tech Science Press  \n| DOI: 10.32604/cmc.2023.043176\u003Cbr>ARTICLE | \u003Cbr> |\n| --- | --- |\n| Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques\u003Cbr>Oscar Peña-Cáceres1 , 2 , *, Henry Silva-Marchan3 , Manuela Albert4 and Miriam Gil1\u003Cbr>1 Professional School of Systems Engineering, Universidad César Vallejo, Piura, 20009, Perú\u003Cbr>2 Escola Tècnica Superior d’Enginyeria, Departament d’Informàtica, Universitat de València, Burjassot, Valencia, 46100, Spain 3 Department of Mathematics, Statistics and Informatics, Universidad Nacional de Tumbes, Tumbes, 24000, Perú 4Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, Valencia, 46022, Spain\u003Cbr>*Corresponding Author: Oscar Peña-Cá[ceres. Email: ojpenac@ucvvirtual.edu.pe](ceres. Email: ojpenac@ucvvirtual.edu.pe)\u003Cbr>[Received: 24 June 2023 Accepted: 11 September 2023 Published: 29 November 2023](Received: 24 June 2023 Accepted: 11 September 2023 Published: 29 November 2023)\u003Cbr>\u003Cbr>ABSTRACT\u003Cbr>The development of artificial intelligence (AI) and smart home technologies has driven the need for speech recognition-based solutions. This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes. Speech recognition allows users to control devices and perform everyday actions through spoken commands, eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light, heating, or lowering the blinds. The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home. It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories, subcategories, and actions from sentences. A dataset labeled with relevant information about categories, subcategories, and actions related to human actions in the smart home is used. The methodology uses machine learning techniques implemented in Python, extracting features using CountVectorizer to convert sentences into numerical representations. The results show that the classification model is able to accurately predict categories, subcategories, and actions based on sentences, with 82.99% accuracy for category, 76.19% accuracy for subcategory, and 90.28% accuracy for action. The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home, supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.\u003Cbr>KEYWORDS\u003Cbr>AI; machine learning; smart home; human action recognition |  |\n\n1 Introduction  \nIn recent years, speech recognition of human actions through artificial intelligence has emerged as a promising and rapidly growing field of research. This area focuses on the development of techniques and algorithms that enable machines to understand and recognize the actions humans perform by using only auditory information captured through recording devices. Speech recognition of human actions has applications in various domains, such as virtual assistants, smart homes, security  \nThis work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \n1874 CMC, 2023, vol.77, no.2  \nsystems, human-machine interaction [1], and healthcare. As AI evolves and improves its ability to understand natural language, speech recognition of human actions has become even more relevant and challenging. Human speech action recognition research has benefited greatly from advances in audio signal processing and machine learning [2] . Combining audio signal feature extraction techniques and machine learning algorithms has enabled the development of more accurate and robust human speec","cbCainYPSidL0Zn2","https://ap.wps.com/l/cbCainYPSidL0Zn2","pdf",1028188,1,19,"English","en",105,"# Abstract\n# 1 Introduction","[{\"question\":\"What problem does the study address in smart homes?\",\"answer\":\"It addresses how to recognize human actions in a smart home using speech or voice, by classifying sentences into categories, subcategories, and actions.\"},{\"question\":\"How is the model implemented and what feature representation is used?\",\"answer\":\"The methodology uses Python machine learning and converts sentences into numerical representations using CountVectorizer features.\"},{\"question\":\"What performance results are reported for category, subcategory, and action recognition?\",\"answer\":\"The study reports 82.99% accuracy for category, 76.19% for subcategory, and 90.28% for action prediction based on sentences.\"}]","Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques - Abstract | PDF",1785730803,48,{"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},"recognition-of-human-actions-through-speech-or-voice-using-machine-learning-techniques-abstract","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/recognition-of-human-actions-through-speech-or-voice-using-machine-learning-techniques-abstract/120592/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in smart homes?","Question",{"text":75,"@type":76},"It addresses how to recognize human actions in a smart home using speech or voice, by classifying sentences into categories, subcategories, and actions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the model implemented and what feature representation is used?",{"text":80,"@type":76},"The methodology uses Python machine learning and converts sentences into numerical representations using CountVectorizer features.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance results are reported for category, subcategory, and action recognition?",{"text":84,"@type":76},"The study reports 82.99% accuracy for category, 76.19% for subcategory, and 90.28% for action prediction based on sentences.","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},[93,97,101,105,110,113,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]