A number of studies have been conducted on the improvement of the sound quality of electrical artificial laryngeal speech,the speech produced has been difficult to hear compared to a natural voice.For this reason,it i...A number of studies have been conducted on the improvement of the sound quality of electrical artificial laryngeal speech,the speech produced has been difficult to hear compared to a natural voice.For this reason,it is necessary to effectively improve the frequency characteristics of the input signal.In the present study,to improve the sound quality of vocalization using an electrical artificial larynx,first,a comparison of the frequency characteristics between the real and artificial voices was conducted,and three filters that can make the frequency characteristics of the artificial voice closer to those of a real voice were generated.Then,the influence of these filters on the quality of the artificial voice was investigated via physical measurement and a subjective evaluation experiment targeted at Japanese five vowels.It was found that the intelligibility of artificial/a/and/o/sounds was improved,whereas little improvement was observed in the case of/i/,/u/,and/e/.The obtained results confirmed the effect of optimizing the input signal into the vibration speaker and indicated areas for further improvement.展开更多
College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and students.We develop a prototype of an intellig...College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and students.We develop a prototype of an intelligent voice instructorassistant system for supporting large classes,in which Amazon Web Services,Alexa Voice Services,and self-developed services are used.It uses a scraping service for reading the questions and answers from the past and current course discussion boards,organizes the questions in JavaScript object notation format,and stores them in the database,which can be accessed by Amazon web services Alexa skills.When a voice question from a student comes,Alexa is used for translating the voice sentence into texts.Then,Siamese deep long short-term memory model is introduced to calculate the similarity between the question asked and the questions in the database to find the best-matched answer.Questions with no match will be sent to the instructor,and instructor’s answer will be added into the database.Experiments show that the implemented model achieves promising results that can lead to a practical system.Intelligent voice instructor-assistant system starts with a small set of questions.It can grow through learning and improving when more and more questions are asked and answered.展开更多
How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable i...How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].展开更多
文摘A number of studies have been conducted on the improvement of the sound quality of electrical artificial laryngeal speech,the speech produced has been difficult to hear compared to a natural voice.For this reason,it is necessary to effectively improve the frequency characteristics of the input signal.In the present study,to improve the sound quality of vocalization using an electrical artificial larynx,first,a comparison of the frequency characteristics between the real and artificial voices was conducted,and three filters that can make the frequency characteristics of the artificial voice closer to those of a real voice were generated.Then,the influence of these filters on the quality of the artificial voice was investigated via physical measurement and a subjective evaluation experiment targeted at Japanese five vowels.It was found that the intelligibility of artificial/a/and/o/sounds was improved,whereas little improvement was observed in the case of/i/,/u/,and/e/.The obtained results confirmed the effect of optimizing the input signal into the vibration speaker and indicated areas for further improvement.
基金The authors wish to thank their colleagues and students who were involved in this study and provided valuable implementation and technical support.The research is partly supported by general funding at IoT and Robotics Education Lab and FURI program at Arizona State University and is partly supported by China Scholarship Council,Guangdong Science and Technology Department,under Grant Number 2016A010101020,2016A010101021,and 2016A010101022Guangzhou Science and Information Bureau under Grant Number 201802010033.
文摘College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and students.We develop a prototype of an intelligent voice instructorassistant system for supporting large classes,in which Amazon Web Services,Alexa Voice Services,and self-developed services are used.It uses a scraping service for reading the questions and answers from the past and current course discussion boards,organizes the questions in JavaScript object notation format,and stores them in the database,which can be accessed by Amazon web services Alexa skills.When a voice question from a student comes,Alexa is used for translating the voice sentence into texts.Then,Siamese deep long short-term memory model is introduced to calculate the similarity between the question asked and the questions in the database to find the best-matched answer.Questions with no match will be sent to the instructor,and instructor’s answer will be added into the database.Experiments show that the implemented model achieves promising results that can lead to a practical system.Intelligent voice instructor-assistant system starts with a small set of questions.It can grow through learning and improving when more and more questions are asked and answered.
文摘How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].
