Valli Madhavi Koti, Krishna Murthy, M. Suganya, Meduri Sridhar Sarma, Gollakota V S S Seshu Kumar, Balamurugan N
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.
从口语中检测情感（SER）是一项检测口语中潜在情感的任务。这是一项极具挑战性的任务，因为情感是主观的，而且与语境高度相关。机器学习算法已被广泛用于 SER，高斯混合模型 (GMM) 算法就是其中之一。GMM 算法是一种统计模型，它将随机变量的概率分布表示为高斯分布之和。它已被广泛用于语音识别和分类任务。在本文中，我们提供了一种使用极端机器学习（EML）和 GMM 算法的 SER 方法。EML 是机器学习的一种类型，它利用随机化以较低的计算成本达到较高的准确度。它已被有效地应用于各种分类任务中。计划中的方法包括两个步骤：特征提取和情感分类。为了提取特征，使用了旋律频率倒频谱系数（MFCC）。MFCC 通常用于语音处理，代表语音信号的频谱包络。GMM 算法用于情感分类。输入特征被建模为高斯混合物，根据输入特征属于每个高斯的可能性对情绪进行分类。在柏林情感语音数据库（EMO-DB）上对所建议的方法进行了测量，准确率达到 74.33%。总之，所建议的使用 EML 和 GMM 算法的 SER 方法显示出良好的效果。这是一种计算效率高、效果好的 SER 方法，可用于各种应用，如虚拟助理的语音情感检测、呼叫中心分析和心理治疗中的情感分析。
{"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}
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.
微生物无处不在，对医疗保健、环境监测和生物技术等各个领域都有重大影响。对于临床微生物学、农业和食品生产等不同领域的专业人员来说，微生物的准确分类和鉴定至关重要。传统的微生物分析方法，如培养技术和人工显微镜检查，可能需要大量人力、成本高昂，有时还会因为不同物种之间的形态相似性而导致分析不充分。因此，人们越来越需要智能图像识别系统来自动完成微生物分类程序，同时尽量减少人工参与。在本文中，我们利用一个包含八种不同微生物类型的数据集，对用于精确识别和分类微生物图像的 ML 和 DL 视角进行了深入分析：球形细菌、变形虫、水螅、巴拉米虫、杆状细菌、螺旋细菌、优生菌和酵母菌。我们采用了 SVM、随机森林和 KNN 等多种毫秒算法，以及深度学习算法 CNN。在这些方法中，CNN 方法的准确率最高。我们深入探讨了当前的技术、挑战和进步，强调了取得进一步进展的机会。
{"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：