[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117740-en":3,"doc-seo-117740-105":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},117740,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",6,"Technology","Machine learning for Arabic phonemes recognition using electrolarynx speech","Arabic phoneme recognition for electrolarynx speech is addressed through a supervised machine learning framework designed to identify which model best classifies phonemes produced by an electrolarynx device. The study compares multiple schemes, including CNN, RNN, ANN, random forest, XGBoost, and LSTM, using Mel frequency cepstral coefficients (MFCC) as speech features. Training and testing rely on Modern Standard Arabic and an aligned dataset recorded by the same speaker, including both ordinary and electrolarynx speech. Results highlight ANN with 75% accuracy, 77% precision, and 21.85% phoneme error rate.","Machine learning for Arabic phonemes recognition using  \nelectrolarynx speech  \nZinah Jaffar Mohammed Ameen, Abdulkareem Abdulrahman Kadhim  \nCollege of Information Engineering, Al-Nahrain University, Baghdad, Iraq  \nArticle history:  \nReceived Jan 21, 2022 Revised Aug 3, 2022 Accepted Aug 28, 2022  \nKeywords:  \nElectrolarynx speech Machine learning Mel frequency cepstral coefficients Performance evaluation  \nCorresponding Author:  \nAutomatic speech recognition system is one of the essential ways of interaction with machines. Interests in speech based intelligent systems have grown in the past few decades. Therefore, there is a need to develop more efficient methods for human speech recognition to ensure the reliability of communication between individuals and machines. This paper is concerned with Arabic phoneme recognition of electrolarynx device. Electrolarynx is a device used by cancer patients having vocal laryngeal cords removed. Speech recognition here is considered to find the preferred machine learning model that can classify phonemes produced by electrolarynx device. The phonemes recognition employs different machine learning schemes, including convolutional neural network, recurrent neural network, artificial neural network (ANN), random forest, extreme gradient boosting (XGBoost), and long short-term memory. Modern standard Arabic is utilized for testing and training phases of the recognition system. The dataset covers both an ordinary speech and electrolarynx device speech recorded by the same person. Mel frequency cepstral coefficients are considered as speech features. The results show that the ANN machine learning method outperformed other methods with an accuracy rate of 75%, a precision value of 77%, and a phoneme error rate (PER) of 21.85% .  \nThis is an open access article under the CC BY-SA license.  \nZinah Jaffar Mohammed Ameen  \nCollege of Information Engineering, Al-Nahrain University Baghdad, Iraq  \n[Email: Zinah.jaffar@coie-nahrain.edu.iq](Email: Zinah.jaffar@coie-nahrain.edu.iq)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nSpeech recognition is a technique of converting spoken words into writing. Every spoken word is a composition of the most basic symbols of a particular language. Phonemes are the smallest units of each language. The method of recognizing simple units of phonemes is critical for developing speech recognition systems [1] . The language model and the acoustic model are two main components of each speech recognition system. A phoneme recognizer ’s accuracy is crucial to the acoustic model ’s accuracy [2] . In order to avoid large vocabulary word size, phoneme-based speech recognition is used, because words may be generated by combining the phonemes of the language. Due to the finite number of phonemes in each language, the process requires a substantial amount of training data compared to word-based models. Lowering complexity of the system allows the use of neural network (NN) often used in speech recognition systems. A neural network is a kind of machine learning technique which is based on the human nerve system and brain structure [3] . Machine-learning methods have recently encountered increasing attention due to their structure which is able to extract robust latent characteristics that allow various recognition algorithms to show generalization in a variety of applications.  \nPipiras et al. [4] proposed a Lithuanian language phonemes recognition system. The system used deep learning techniques to recognize spoken words based on their phoneme sequences. Encoder/decoder model for feed-forward beside recurrent neural network (RNN) architecture were used and the performance was measured in isolated speech recognition tasks, yielding an overall recognition accuracy of 99.3% as well as long-phrase recognition tasks with an accuracy of 99.2% . The given rates were for pure Lithuanian phoneme sequences, without considering any added noise or disturbances.  \nA hidden Markovian model (HMM)","cbCairGs8kU32dSP","https://ap.wps.com/l/cbCairGs8kU32dSP","pdf",776348,1,13,"English","en",105,"# ABSTRACT\n# INTRODUCTION\n## Speech recognition and phoneme-based modeling\n## Neural networks and machine learning motivation\n## Related work on phoneme recognition systems","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To recognize Arabic phonemes from electrolarynx speech and determine the most effective machine learning model for classifying those phonemes.\"},{\"question\":\"Which machine learning models are compared?\",\"answer\":\"The paper compares CNN, RNN, ANN, random forest, XGBoost, and LSTM approaches for phoneme classification.\"},{\"question\":\"What features and language are used for training and testing?\",\"answer\":\"Mel frequency cepstral coefficients (MFCC) are used as speech features, and Modern Standard Arabic is used during both training and testing.\"}]","Machine learning for Arabic phonemes recognition using electrolarynx speech | PDF",1785679292,33,{"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},"machine-learning-for-arabic-phonemes-recognition-using-electrolarynx-speech","",{"@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/machine-learning-for-arabic-phonemes-recognition-using-electrolarynx-speech/117740/",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","2026-08-02",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 is the main goal of the study?","Question",{"text":75,"@type":76},"To recognize Arabic phonemes from electrolarynx speech and determine the most effective machine learning model for classifying those phonemes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared?",{"text":80,"@type":76},"The paper compares CNN, RNN, ANN, random forest, XGBoost, and LSTM approaches for phoneme classification.",{"name":82,"@type":73,"acceptedAnswer":83},"What features and language are used for training and testing?",{"text":84,"@type":76},"Mel frequency cepstral coefficients (MFCC) are used as speech features, and Modern Standard Arabic is used during both training and testing.","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":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]