Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning areas.Not only is it complicated,but it has also become a pressing necessity,considering how social me...Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning areas.Not only is it complicated,but it has also become a pressing necessity,considering how social media has become an integral part of adolescents’lives and how serious the impacts of cyberbullying and online harassment can be,particularly among teenagers.This paper contains a systematic literature review of modern strategies,machine learning methods,and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet.We undertake an in-depth review of 13 papers from four scientific databases.The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing.In this review,we consider a cyberbullying detection framework on social media platforms,which includes data collection,data processing,feature selection,feature extraction,and the application ofmachine learning to classify whether texts contain cyberbullying or not.This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction,allowing future solutions to be more practical and effective.展开更多
Speech recognition systems have become a unique human-computer interaction(HCI)family.Speech is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computa...Speech recognition systems have become a unique human-computer interaction(HCI)family.Speech is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation experience.This paper aims to present a retrospective yet modern approach to the world of speech recognition systems.The development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this domain.This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing.Since speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.展开更多
Scientific and technology are the first productivity. They are the revolutionary power of accelerating the civilization progress of the human society. In order to realize the goal set in the 5th Plenary Session of... Scientific and technology are the first productivity. They are the revolutionary power of accelerating the civilization progress of the human society. In order to realize the goal set in the 5th Plenary Session of the 16th Central Committee of the Communist Part of China, we must adhere to the Deng Xiaoping Theory and the guideline of Three Represents,comprehensively implement the guideline of Scientific Development,adopt the Strategy of Developing the Country wirh Science and Education and the Strategy of Talents Strengthen the Country, further exert the important role of the scientific and technology progress and innovation, so as to lead the economic and social development into the orbit of Human First and comprehensive and sustaining development.……展开更多
Classification of speech signals is a vital part of speech signal processing systems.With the advent of speech coding and synthesis,the classification of the speech signal is made accurate and faster.Conventional meth...Classification of speech signals is a vital part of speech signal processing systems.With the advent of speech coding and synthesis,the classification of the speech signal is made accurate and faster.Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification.In this paper,we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations.Prior classification of speech signals,the study extracts the essential features from the speech signal using Cepstral Analysis.The features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data rate.Hence to improve the precision of classification,Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral coefficient.The classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new datasets.The validation of testing sets is evaluated using RL that provides feedback to Classifiers.Finally,at the user interface,the signals are played by decoding the signal after being retrieved from the classifier back based on the input query.The results are evaluated in the form of accuracy,recall,precision,f-measure,and error rate,where generative adversarial network attains an increased accuracy rate than other methods:Multi-Layer Perceptron,Recurrent Neural Networks,Deep belief Networks,and Convolutional Neural Networks.展开更多
Every public speaker prepares his or her public speech meticulously.Witty remarks emerge in an endless stream,and demonstrate the rhetoric beauty of English to a great extent.Almost every speaker employs parallelism i...Every public speaker prepares his or her public speech meticulously.Witty remarks emerge in an endless stream,and demonstrate the rhetoric beauty of English to a great extent.Almost every speaker employs parallelism in his or her public speeches.The present paper is intended to study the concept,the classification and the significance of parallelism in English.展开更多
目的分析国际言语失用症领域研究的热点和发展趋势。方法检索2004年1月至2023年12月Web of Science核心合集数据库中言语失用症相关文献,采用CiteSpace 6.2 R进行分析。结果共纳入893篇文献,发文量呈波动上升趋势。美国和澳大利亚在言...目的分析国际言语失用症领域研究的热点和发展趋势。方法检索2004年1月至2023年12月Web of Science核心合集数据库中言语失用症相关文献,采用CiteSpace 6.2 R进行分析。结果共纳入893篇文献,发文量呈波动上升趋势。美国和澳大利亚在言语失用症领域具有较高的影响力。梅奥诊所和悉尼大学具有高中心性,Joseph R Duffy的发文量位居前列,近5年的热点主题主要集中在帕金森病、言语失用症的分类与治疗强度方面。结论言语失用症的研究热度呈上升趋势,虽然在神经影像学、临床声学评估与发音-运动学干预等方面取得了显著成果,但未来仍需进一步探索言语失用症的病理机制和治疗方法。展开更多
目的:针对野战噪声条件下便携式野战医疗装备的语音交互性能受到影响的问题,设计一种小尺寸双麦前端系统。方法:该系统基于最小二乘准则实现小尺寸双麦波速形成,进而实现前端语音增强。系统硬件主要由双麦、信号预处理模块、嵌入式处理...目的:针对野战噪声条件下便携式野战医疗装备的语音交互性能受到影响的问题,设计一种小尺寸双麦前端系统。方法:该系统基于最小二乘准则实现小尺寸双麦波速形成,进而实现前端语音增强。系统硬件主要由双麦、信号预处理模块、嵌入式处理器、模拟数字转换器(analog to digital converter,ADC)、数字模拟转换器(digital to analog converter,DAC)、供电模块等组成。其中,双麦采用2个贴片式微机电系统(micro electro mechanical system,MEMS)麦克风,信号预处理模块、ADC、DAC内置在通用音频编码器WM8978中,嵌入式处理器采用STM32F405系列处理器,供电模块采用LM1117电压调节器芯片。系统软件采用KeilμVision4开发软件编译和测试。为验证该系统的性能,进行指向性实验和语音增强实验。结果:指向性实验结果表明,在0.5~2.0 kHz频率范围内,该系统在各频点的指向性一致性较好;语音增强实验结果表明,在枪声、监护仪报警、医疗器皿碰撞3类非平稳噪声条件下,该系统可有效提升语音的音质及识别率。结论:该系统能实现语音增强,可为便携式野战医疗装备的语音交互提供有效的支持。展开更多
文摘Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning areas.Not only is it complicated,but it has also become a pressing necessity,considering how social media has become an integral part of adolescents’lives and how serious the impacts of cyberbullying and online harassment can be,particularly among teenagers.This paper contains a systematic literature review of modern strategies,machine learning methods,and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet.We undertake an in-depth review of 13 papers from four scientific databases.The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing.In this review,we consider a cyberbullying detection framework on social media platforms,which includes data collection,data processing,feature selection,feature extraction,and the application ofmachine learning to classify whether texts contain cyberbullying or not.This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction,allowing future solutions to be more practical and effective.
文摘Speech recognition systems have become a unique human-computer interaction(HCI)family.Speech is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation experience.This paper aims to present a retrospective yet modern approach to the world of speech recognition systems.The development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this domain.This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing.Since speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.
文摘 Scientific and technology are the first productivity. They are the revolutionary power of accelerating the civilization progress of the human society. In order to realize the goal set in the 5th Plenary Session of the 16th Central Committee of the Communist Part of China, we must adhere to the Deng Xiaoping Theory and the guideline of Three Represents,comprehensively implement the guideline of Scientific Development,adopt the Strategy of Developing the Country wirh Science and Education and the Strategy of Talents Strengthen the Country, further exert the important role of the scientific and technology progress and innovation, so as to lead the economic and social development into the orbit of Human First and comprehensive and sustaining development.……
文摘Classification of speech signals is a vital part of speech signal processing systems.With the advent of speech coding and synthesis,the classification of the speech signal is made accurate and faster.Conventional methods are considered inaccurate due to the uncertainty and diversity of speech signals in the case of real speech signal classification.In this paper,we use efficient speech signal classification using a series of neural network classifiers with reinforcement learning operations.Prior classification of speech signals,the study extracts the essential features from the speech signal using Cepstral Analysis.The features are extracted by converting the speech waveform to a parametric representation to obtain a relatively minimized data rate.Hence to improve the precision of classification,Generative Adversarial Networks are used and it tends to classify the speech signal after the extraction of features from the speech signal using the cepstral coefficient.The classifiers are trained with these features initially and the best classifier is chosen to perform the task of classification on new datasets.The validation of testing sets is evaluated using RL that provides feedback to Classifiers.Finally,at the user interface,the signals are played by decoding the signal after being retrieved from the classifier back based on the input query.The results are evaluated in the form of accuracy,recall,precision,f-measure,and error rate,where generative adversarial network attains an increased accuracy rate than other methods:Multi-Layer Perceptron,Recurrent Neural Networks,Deep belief Networks,and Convolutional Neural Networks.
文摘Every public speaker prepares his or her public speech meticulously.Witty remarks emerge in an endless stream,and demonstrate the rhetoric beauty of English to a great extent.Almost every speaker employs parallelism in his or her public speeches.The present paper is intended to study the concept,the classification and the significance of parallelism in English.
文摘目的分析国际言语失用症领域研究的热点和发展趋势。方法检索2004年1月至2023年12月Web of Science核心合集数据库中言语失用症相关文献,采用CiteSpace 6.2 R进行分析。结果共纳入893篇文献,发文量呈波动上升趋势。美国和澳大利亚在言语失用症领域具有较高的影响力。梅奥诊所和悉尼大学具有高中心性,Joseph R Duffy的发文量位居前列,近5年的热点主题主要集中在帕金森病、言语失用症的分类与治疗强度方面。结论言语失用症的研究热度呈上升趋势,虽然在神经影像学、临床声学评估与发音-运动学干预等方面取得了显著成果,但未来仍需进一步探索言语失用症的病理机制和治疗方法。
文摘目的:针对野战噪声条件下便携式野战医疗装备的语音交互性能受到影响的问题,设计一种小尺寸双麦前端系统。方法:该系统基于最小二乘准则实现小尺寸双麦波速形成,进而实现前端语音增强。系统硬件主要由双麦、信号预处理模块、嵌入式处理器、模拟数字转换器(analog to digital converter,ADC)、数字模拟转换器(digital to analog converter,DAC)、供电模块等组成。其中,双麦采用2个贴片式微机电系统(micro electro mechanical system,MEMS)麦克风,信号预处理模块、ADC、DAC内置在通用音频编码器WM8978中,嵌入式处理器采用STM32F405系列处理器,供电模块采用LM1117电压调节器芯片。系统软件采用KeilμVision4开发软件编译和测试。为验证该系统的性能,进行指向性实验和语音增强实验。结果:指向性实验结果表明,在0.5~2.0 kHz频率范围内,该系统在各频点的指向性一致性较好;语音增强实验结果表明,在枪声、监护仪报警、医疗器皿碰撞3类非平稳噪声条件下,该系统可有效提升语音的音质及识别率。结论:该系统能实现语音增强,可为便携式野战医疗装备的语音交互提供有效的支持。
