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Speech Emotion Recognition using Extreme Machine Learning 利用极端机器学习进行语音情感识别

EAI Endorsed Transactions on Internet of Things Pub Date : 2023-11-27 DOI: 10.4108/eetiot.4485

Valli Madhavi Koti, Krishna Murthy, M. Suganya, Meduri Sridhar Sarma, Gollakota V S S Seshu Kumar, Balamurugan N

{"title":"Speech Emotion Recognition using Extreme Machine Learning","authors":"Valli Madhavi Koti, Krishna Murthy, M. Suganya, Meduri Sridhar Sarma, Gollakota V S S Seshu Kumar, Balamurugan N","doi":"10.4108/eetiot.4485","DOIUrl":"https://doi.org/10.4108/eetiot.4485","url":null,"abstract":"Detecting Emotion from Spoken Words (SER) is the task of detecting the underlying emotion in spoken language. It is a challenging task, as emotions are subjective and highly contextual. Machine learning algorithms have been widely used for SER, and one such algorithm is the Gaussian Mixture Model (GMM) algorithm. The GMM algorithm is a statistical model that represents the probability distribution of a random variable as a sum of Gaussian distributions. It has been widely used for speech recognition and classification tasks. In this article, we offer a method for SER using Extreme Machine Learning (EML) with the GMM algorithm. EML is a type of machine learning that uses randomization to achieve high accuracy at a low computational cost. It has been effectively utilised in various classification tasks. For the planned approach includes two steps: feature extraction and emotion classification. Cepstral Coefficients of Melody Frequency (MFCCs) are used in order to extract features. MFCCs are commonly used for speech processing and represent the spectral envelope of the speech signal. The GMM algorithm is used for emotion classification. The input features are modelled as a mixture of Gaussians, and the emotion is classified based on the likelihood of the input features belonging to each Gaussian. Measurements were taken of the suggested method on the The Berlin Database of Emotional Speech (EMO-DB) and achieved an accuracy of 74.33%. In conclusion, the proposed approach to SER using EML and the GMM algorithm shows promising results. It is a computationally efficient and effective approach to SER and can be used in various applications, such as speech-based emotion detection for virtual assistants, call centre analytics, and emotional analysis in psychotherapy.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139233153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements 通过智能图像识别破译微生物：机器学习和深度学习方法、挑战与进步

EAI Endorsed Transactions on Internet of Things Pub Date : 2023-11-27 DOI: 10.4108/eetiot.4484

Syed Khasim, Hritwik Ghosh, Irfan Sadiq Rahat, Kareemulla Shaik, Manava Yesubabu

{"title":"Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements","authors":"Syed Khasim, Hritwik Ghosh, Irfan Sadiq Rahat, Kareemulla Shaik, Manava Yesubabu","doi":"10.4108/eetiot.4484","DOIUrl":"https://doi.org/10.4108/eetiot.4484","url":null,"abstract":"Microorganisms are pervasive and have a significant impact in various fields such as healthcare, environmental monitoring, and biotechnology. Accurate classification and identification of microorganisms are crucial for professionals in diverse areas, including clinical microbiology, agriculture, and food production. Traditional methods for analyzing microorganisms, like culture techniques and manual microscopy, can be labor-intensive, expensive, and occasionally inadequate due to morphological similarities between different species. As a result, there is an increasing need for intelligent image recognition systems to automate microorganism classification procedures with minimal human involvement. In this paper, we present an in-depth analysis of ML and DL perspectives used for the precise recognition and classification of microorganism images, utilizing a dataset comprising eight distinct microorganism types: Spherical bacteria, Amoeba, Hydra, Paramecium, Rod bacteria, Spiral bacteria, Euglena and Yeast. We employed several ml algorithms including SVM, Random Forest, and KNN, as well as the deep learning algorithm CNN. Among these methods, the highest accuracy was achieved using the CNN approach. We delve into current techniques, challenges, and advancements, highlighting opportunities for further progress.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139233904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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