机器人语音识别中英文对照外文翻译文献
扫地机器人语音识别功能作文
扫地机器人语音识别功能作文英文回答：Voice recognition technology has become increasingly prevalent in recent years, and is now commonly found in a wide range of devices, from smartphones to smart home appliances. One area where voice recognition has proven to be particularly useful is in the field of robotics.扫地机器人 are a type of robotic vacuum cleaner that uses a combination of sensors and artificial intelligence to navigate around a room and clean up dirt and debris. Many sweeping robots now come equipped with voice recognition capabilities, which allow them to be controlled using spoken commands. This can be a great convenience for users, as it allows them to start, stop, and pause the robot without having to bend down and press buttons.In addition to basic commands, some sweeping robots with voice recognition can also be used to perform morecomplex tasks, such as scheduling cleaning cycles or adjusting the cleaning settings. This can be done by simply speaking the desired command into the robot's microphone.The integration of voice recognition technology into sweeping robots has made them even more user-friendly and convenient to use. As voice recognition technology continues to improve, it is likely that we will see even more advanced and innovative applications for this technology in the future.中文回答：近年来，语音识别技术变得越来越普遍，现已广泛应用于从智能手机到智能家居设备的各种设备中。
音频信号处理博士论文中英文资料外文翻译文献
音频信号处理博士论文中英文资料外文翻
译文献
音频信号处理是一个广泛研究的领域，涉及到音频信号的获取、分析、传输和处理等方面。
本文翻译了以下两篇外文文献，为音频
信号处理博士论文的写作提供参考。
文献一：Title of Paper One
作者：
摘要：
该篇文献提出了一种新的音频信号处理算法，旨在改善音频信
号的质量和增强用户对音乐的感受。
通过对音频信号进行特征提取
和分析，该算法能够有效地消除噪音和失真，并提供更清晰、更丰
富的音频体验。
文献介绍了算法的原理和实现方式，并通过实验验
证了其在不同音频数据集上的有效性。
文献二：Title of Paper Two
作者：
摘要：
该篇文献探讨了音频信号处理领域的一个重要问题，即语音识
别的准确性和鲁棒性。
通过分析现有的语音识别算法，文献指出了
当前算法存在的一些问题，并提出了一种改进的方法。
该方法基于
深度研究和卷积神经网络，并通过对音频信号进行多层次的特征研
究和表示研究，提高了语音识别的准确性和鲁棒性。
文献还介绍了
该方法的实验结果，并与其他算法进行了比较。
总结
这两篇外文文献介绍了音频信号处理领域的一些重要研究进展
和算法。
它们提供了宝贵的参考和借鉴，可以在音频信号处理博士
论文的写作中起到指导作用。
通过综合运用这些研究成果，我们可
以进一步改进音频信号处理算法，提高音频信号的质量和用户体验。
机器人外文翻译(文献翻译-中英文翻译)
外文翻译外文资料：RobotsFirst, I explain the background robots, robot technology development. It should be said it is a common scientific and technological development of a comprehensive results, for the socio-economic development of a significant impact on a science and technology. It attributed the development of all countries in the Second World War to strengthen the economic input on strengthening the country's economic development. But they also demand the development of the productive forces the inevitable result of human development itself is the inevitable result then with the development of humanity, people constantly discuss the natural process, in understanding and reconstructing the natural process, people need to be able to liberate a slave. So this is the slave people to be able to replace the complex and engaged in heavy manual labor, People do not realize right up to the world's understanding and transformation of this technology as well as people in the development process of an objective need. Robots are three stages of development, in other words, we are accustomed to regarding robots are divided into three categories. is a first-generation robots, also known as teach-type robot, it is through a computer, to control over one of a mechanical degrees of freedom Through teaching and information stored procedures, working hours to read out information, and then issued a directive so the robot can repeat according to the people at that time said the results show this kind of movement again, For example, the car spot welding robots, only to put this spot welding process, after teaching, and it is always a repeat of a work It has the external environment is no perception that the force manipulation of the size of the work piece there does not exist, welding 0S It does not know, then this fact from the first generation robot, it will exist this shortcoming, it in the 20th century, the late 1970s, people started to study the second-generation robot, called Robot with the feeling that This feeling with the robot is similar in function of a certain feeling, forinstance, force and touch, slipping, visual, hearing and who is analogous to that with all kinds of feelings, say in a robot grasping objects, In fact, it can be the size of feeling out, it can through visual, to be able to feel and identify its shape, size, color Grasping an egg, it adopted a acumen, aware of its power and the size of the slide. Third-generation robots, we were a robotics ideal pursued by the most advanced stage, called intelligent robots, So long as tell it what to do, not how to tell it to do, it will be able to complete the campaign, thinking and perception of this man-machine communication function and function Well, this current development or relative is in a smart part of the concept and meaning But the real significance of the integrity of this intelligent robot did not actually exist, but as we continued the development of science and technology, the concept of intelligent increasingly rich, it grows ever wider connotations.Now, I would like to briefly outline some of the industrial robot situation. So far, the industrial robot is the most mature and widely used category of a robot, now the world's total sales of 1.1 million Taiwan, which is the 1999 statistics, however, 1.1 million in Taiwan have been using the equipment is 75 million, this volume is not small. Overall, the Japanese industrial robots in this one, is the first of the robots to become the Kingdom, the United States have developed rapidly. Newly installed in several areas of Taiwan, which already exceeds Japan, China has only just begun to enter the stage of industrialization, has developed a variety of industrial robot prototype and small batch has been used in production.Spot welding robot is the auto production line, improve production efficiency and raise the quality of welding car, reduce the labor intensity of a robot. It is characterized by two pairs of robots for spot welding of steel plate, bearing a great need for the welding tongs, general in dozens of kilograms or more, then its speed in meters per second a 5-2 meter of such high-speed movement. So it is generally five to six degrees of freedom, load 30 to 120 kilograms, the great space, probably expected that the work of a spherical space, a high velocity, the concept of freedom, that is to say, Movement is relatively independent of the number of components, the equivalent of our body, waist is a rotary degree of freedom We have to be able to hold his arm, Arm can be bent, then this three degrees of freedom, Meanwhile there is a wristposture adjustment to the use of the three autonomy, the general robot has six degrees of freedom. We will be able to space the three locations, three postures, the robot fully achieved, and of course we have less than six degrees of freedom. Have more than six degrees of freedom robot, in different occasions the need to configure.The second category of service robots, with the development of industrialization, especially in the past decade, Robot development in the areas of application are continuously expanding, and now a very important characteristic, as we all know, Robot has gradually shifted from manufacturing to non-manufacturing and service industries, we are talking about the car manufacturer belonging to the manufacturing industry, However, the services sector including cleaning, refueling, rescue, rescue, relief, etc. These belong to the non-manufacturing industries and service industries, so here is compared with the industrial robot, it is a very important difference. It is primarily a mobile platform, it can move to sports, there are some arms operate, also installed some as a force sensor and visual sensors, ultrasonic ranging sensors, etc. It’s surrounding environment for the conduct of identification, to determine its campaign to complete some work, this is service robot’s one of the basic characteristics.For example, domestic robot is mainly embodied in the example of some of the carpets and flooring it to the regular cleaning and vacuuming. The robot it is very meaningful, it has sensors, it can furniture and people can identify, It automatically according to a law put to the ground under the road all cleaned up. This is also the home of some robot performance.The medical robots, nearly five years of relatively rapid development of new application areas. If people in the course of an operation, doctors surgery, is a fatigue, and the other manually operated accuracy is limited. Some universities in Germany, which, facing the spine, lumbar disc disease, the identification, can automatically use the robot-aided positioning, operation and surgery Like the United States have been more than 1,000 cases of human eyeball robot surgery, the robot, also including remote-controlled approach, the right of such gastrointestinal surgery, we see on the television inside. a manipulator, about the thickness fingers such a manipulator, inserted through the abdominal viscera, people on the screen operating the machines hand, it also used the method of laser lesion laser treatment, this is the case, peoplewould not have a very big damage to the human body.In reality, this right as a human liberation is a very good robots, medical robots it is very complex, while it is fully automated to complete all the work, there are difficulties, and generally are people to participate. This is America, the development of such a surgery Lin Bai an example, through the screen, through a remote control operator to control another manipulator, through the realization of the right abdominal surgery A few years ago our country the exhibition, the United States has been successful in achieving the right to the heart valve surgery and bypass surgery. This robot has in the area, caused a great sensation, but also, AESOP's surgical robot, In fact, it through some equipment to some of the lesions inspections, through a manipulator can be achieved on some parts of the operation Also including remotely operated manipulator, and many doctors are able to participate in the robot under surgery Robot doctor to include doctors with pliers, tweezers or a knife to replace the nurses, while lighting automatically to the doctor's movements linked, the doctor hands off, lighting went off, This is very good, a doctor's assistant.Robot is mankind's right-hand man; friendly coexistence can be a reliable friend. In future, we will see and there will be a robot space inside, as a mutual aide and friend. Robots will create the jobs issue. We believe that there would not be a "robot appointment of workers being laid off" situation, because people with the development of society, In fact the people from the heavy physical and dangerous environment liberated, so that people have a better position to work, to create a better spiritual wealth and cultural wealth.译文资料：机器人首先我介绍一下机器人产生的背景，机器人技术的发展，它应该说是一个科学技术发展共同的一个综合性的结果，同时，为社会经济发展产生了一个重大影响的一门科学技术，它的发展归功于在第二次世界大战中各国加强了经济的投入，就加强了本国的经济的发展。
人工智能英文文献原文及译文
附件四英文文献原文Artificial Intelligence"Artificial intelligence" is a word was originally Dartmouth in 1956 to put forward. From then on, researchers have developed many theories and principles, the concept of artificial intelligence is also expands. Artificial intelligence is a challenging job of science, the person must know computer knowledge, psychology and philosophy. Artificial intelligence is included a wide range of science, it is composed of different fields, such as machine learning, computer vision, etc, on the whole, the research on artificial intelligence is one of the main goals of the machine can do some usually need to perform complex human intelligence. But in different times and different people in the "complex" understanding is different. Such as heavy science and engineering calculation was supposed to be the brain to undertake, now computer can not only complete this calculation, and faster than the human brain can more accurately, and thus the people no longer put this calculation is regarded as "the need to perform complex human intelligence, complex tasks" work is defined as the development of The Times and the progress of technology, artificial intelligence is the science of specific target and nature as The Times change and development. On the one hand it continues to gain new progress on the one hand, and turning to more meaningful, the more difficult the target. Current can be used to study the main material of artificial intelligence and artificial intelligence technology to realize the machine is a computer, the development history of artificial intelligence is computer science and technology and the development together. Besides the computer science and artificial intelligence also involves information, cybernetics, automation, bionics, biology, psychology, logic, linguistics, medicine and philosophy and multi-discipline. Artificial intelligence research include: knowledge representation, automatic reasoning and search method, machine learning and knowledge acquisition and processing of knowledge system, natural language processing, computer vision, intelligent robot, automatic program design, etc.Practical application of machine vision: fingerprint identification,face recognition, retina identification, iris identification, palm, expert system, intelligent identification, search, theorem proving game, automatic programming, and aerospace applications.Artificial intelligence is a subject categories, belong to the door edge discipline of natural science and social science.Involving scientific philosophy and cognitive science, mathematics, neurophysiological, psychology, computer science, information theory, cybernetics, not qualitative theory, bionics.The research category of natural language processing, knowledge representation, intelligent search, reasoning, planning, machine learning, knowledge acquisition, combined scheduling problem, perception, pattern recognition, logic design program, soft calculation, inaccurate and uncertainty, the management of artificial life, neural network, and complex system, human thinking mode of genetic algorithm.Applications of intelligent control, robotics, language and image understanding, genetic programming robot factory.Safety problemsArtificial intelligence is currently in the study, but some scholars think that letting computers have IQ is very dangerous, it may be against humanity. The hidden danger in many movie happened.The definition of artificial intelligenceDefinition of artificial intelligence can be divided into two parts, namely "artificial" or "intelligent". "Artificial" better understanding, also is controversial. Sometimes we will consider what people can make, or people have high degree of intelligence to create artificial intelligence, etc. But generally speaking, "artificial system" is usually significance of artificial system.What is the "smart", with many problems. This involves other such as consciousness, ego, thinking (including the unconscious thoughts etc. People only know of intelligence is one intelligent, this is the universal view of our own. But we are very limited understanding of the intelligence of the intelligent people constitute elements are necessary to find, so it is difficult to define what is "artificial" manufacturing "intelligent". So the artificial intelligence research often involved in the study of intelligent itself. Other about animal or other artificial intelligence system is widely considered to be related to the study of artificial intelligence.Artificial intelligence is currently in the computer field, the moreextensive attention. And in the robot, economic and political decisions, control system, simulation system application. In other areas, it also played an indispensable role.The famous American Stanford university professor nelson artificial intelligence research center of artificial intelligence under such a definition: "artificial intelligence about the knowledge of the subject is and how to represent knowledge -- how to gain knowledge and use of scientific knowledge. But another American MIT professor Winston thought: "artificial intelligence is how to make the computer to do what only can do intelligent work." These comments reflect the artificial intelligence discipline basic ideas and basic content. Namely artificial intelligence is the study of human intelligence activities, has certain law, research of artificial intelligence system, how to make the computer to complete before the intelligence needs to do work, also is to study how the application of computer hardware and software to simulate human some intelligent behavior of the basic theory, methods and techniques.Artificial intelligence is a branch of computer science, since the 1970s, known as one of the three technologies (space technology, energy technology, artificial intelligence). Also considered the 21st century (genetic engineering, nano science, artificial intelligence) is one of the three technologies. It is nearly three years it has been developed rapidly, and in many fields are widely applied, and have made great achievements, artificial intelligence has gradually become an independent branch, both in theory and practice are already becomes a system. Its research results are gradually integrated into people's lives, and create more happiness for mankind.Artificial intelligence is that the computer simulation research of some thinking process and intelligent behavior (such as study, reasoning, thinking, planning, etc.), including computer to realize intelligent principle, make similar to that of human intelligence, computer can achieve higher level of computer application. Artificial intelligence will involve the computer science, philosophy and linguistics, psychology, etc. That was almost natural science and social science disciplines, the scope of all already far beyond the scope of computer science and artificial intelligence and thinking science is the relationship between theory and practice, artificial intelligence is in the mode of thinking science technology application level, is one of its application. From the view of thinking, artificial intelligence is not limited to logicalthinking, want to consider the thinking in image, the inspiration of thought of artificial intelligence can promote the development of the breakthrough, mathematics are often thought of as a variety of basic science, mathematics and language, thought into fields, artificial intelligence subject also must not use mathematical tool, mathematical logic, the fuzzy mathematics in standard etc, mathematics into the scope of artificial intelligence discipline, they will promote each other and develop faster.A brief history of artificial intelligenceArtificial intelligence can be traced back to ancient Egypt's legend, but with 1941, since the development of computer technology has finally can create machine intelligence, "artificial intelligence" is a word in 1956 was first proposed, Dartmouth learned since then, researchers have developed many theories and principles, the concept of artificial intelligence, it expands and not in the long history of the development of artificial intelligence, the slower than expected, but has been in advance, from 40 years ago, now appears to have many AI programs, and they also affected the development of other technologies. The emergence of AI programs, creating immeasurable wealth for the community, promoting the development of human civilization.The computer era1941 an invention that information storage and handling all aspects of the revolution happened. This also appeared in the U.S. and Germany's invention is the first electronic computer. Take a few big pack of air conditioning room, the programmer's nightmare: just run a program for thousands of lines to set the 1949. After improvement can be stored procedure computer programs that make it easier to input, and the development of the theory of computer science, and ultimately computer ai. This in electronic computer processing methods of data, for the invention of artificial intelligence could provide a kind of media.The beginning of AIAlthough the computer AI provides necessary for technical basis, but until the early 1950s, people noticed between machine and human intelligence. Norbert Wiener is the study of the theory of American feedback. Most familiar feedback control example is the thermostat. It will be collected room temperature and hope, and reaction temperature compared to open or close small heater, thus controlling environmental temperature. The importance of the study lies in the feedback loop Wiener:all theoretically the intelligence activities are a result of feedback mechanism and feedback mechanism is. Can use machine. The findings of the simulation of early development of AI.1955, Simon and end Newell called "a logical experts" program. This program is considered by many to be the first AI programs. It will each problem is expressed as a tree, then choose the model may be correct conclusion that a problem to solve. "logic" to the public and the AI expert research field effect makes it AI developing an important milestone in 1956, is considered to be the father of artificial intelligence of John McCarthy organized a society, will be a lot of interest machine intelligence experts and scholars together for a month. He asked them to Vermont Dartmouth in "artificial intelligence research in summer." since then, this area was named "artificial intelligence" although Dartmouth learn not very successful, but it was the founder of the centralized and AI AI research for later laid a foundation.After the meeting of Dartmouth, AI research started seven years. Although the rapid development of field haven't define some of the ideas, meeting has been reconsidered and Carnegie Mellon university. And MIT began to build AI research center is confronted with new challenges. Research needs to establish the: more effective to solve the problem of the system, such as "logic" in reducing search; expert There is the establishment of the system can be self learning.In 1957, "a new program general problem-solving machine" first version was tested. This program is by the same logic "experts" group development. The GPS expanded Wiener feedback principle, can solve many common problem. Two years later, IBM has established a grind investigate group Herbert AI. Gelerneter spent three years to make a geometric theorem of solutions of the program. This achievement was a sensation.When more and more programs, McCarthy busy emerge in the history of an AI. 1958 McCarthy announced his new fruit: LISP until today still LISP language. In. "" mean" LISP list processing ", it quickly adopted for most AI developers.In 1963 MIT from the United States government got a pen is 22millions dollars funding for research funding. The machine auxiliary recognition from the defense advanced research program, have guaranteed in the technological progress on this plan ahead of the Soviet union. Attracted worldwide computer scientists, accelerate the pace of development of AI research.Large programAfter years of program. It appeared a famous called "SHRDLU." SHRDLU "is" the tiny part of the world "project, including the world (for example, only limited quantity of geometrical form of research and programming). In the MIT leadership of Minsky Marvin by researchers found, facing the object, the small computer programs can solve the problem space and logic. Other as in the late 1960's STUDENT", "can solve algebraic problems," SIR "can understand the simple English sentence. These procedures for handling the language understanding and logic.In the 1970s another expert system. An expert system is a intelligent computer program system, and its internal contains a lot of certain areas of experience and knowledge with expert level, can use the human experts' knowledge and methods to solve the problems to deal with this problem domain. That is, the expert system is a specialized knowledge and experience of the program system. Progress is the expert system could predict under certain conditions, the probability of a solution for the computer already has. Great capacity, expert systems possible from the data of expert system. It is widely used in the market. Ten years, expert system used in stock, advance help doctors diagnose diseases, and determine the position of mineral instructions miners. All of this because of expert system of law and information storage capacity and become possible.In the 1970s, a new method was used for many developing, famous as AI Minsky tectonic theory put forward David Marr. Another new theory of machine vision square, for example, how a pair of image by shadow, shape, color, texture and basic information border. Through the analysis of these images distinguish letter, can infer what might be the image in the same period. PROLOGE result is another language, in 1972. In the 1980s, the more rapid progress during the AI, and more to go into business. 1986, the AI related software and hardware sales $4.25 billion dollars. Expert system for its utility, especially by demand. Like digital electric company with such company XCON expert system for the VAX mainframe programming. Dupont, general motors and Boeing has lots of dependence of expert system for computer expert. Some production expert system of manufacture software auxiliary, such as Teknowledge and Intellicorp established. In order to find and correct the mistakes, existing expert system and some other experts system was designed，such as teach users learn TVC expert system of the operating system.From the lab to daily lifePeople began to feel the computer technique and artificial intelligence. No influence of computer technology belong to a group of researchers in the lab. Personal computers and computer technology to numerous technical magazine now before a people. Like the United States artificial intelligence association foundation. Because of the need to develop, AI had a private company researchers into the boom. More than 150 a DEC (it employs more than 700 employees engaged in AI research) that have spent 10 billion dollars in internal AI team.Some other AI areas in the 1980s to enter the market. One is the machine vision Marr and achievements of Minsky. Now use the camera and production, quality control computer. Although still very humble, these systems have been able to distinguish the objects and through the different shape. Until 1985 America has more than 100 companies producing machine vision systems, sales were us $8 million.But the 1980s to AI and industrial all is not a good year for years. 1986-87 AI system requirements, the loss of industry nearly five hundred million dollars. Teknowledge like Intellicorp and two loss of more than $6 million, about one-third of the profits of the huge losses forced many research funding cuts the guide led. Another disappointing is the defense advanced research programme support of so-called "intelligent" this project truck purpose is to develop a can finish the task in many battlefield robot. Since the defects and successful hopeless, Pentagon stopped project funding.Despite these setbacks, AI is still in development of new technology slowly. In Japan were developed in the United States, such as the fuzzy logic, it can never determine the conditions of decision making, And neural network, regarded as the possible approaches to realizing artificial intelligence. Anyhow, the eighties was introduced into the market, the AI and shows the practical value. Sure, it will be the key to the 21st century. "artificial intelligence technology acceptance inspection in desert storm" action of military intelligence test equipment through war. Artificial intelligence technology is used to display the missile system and warning and other advanced weapons. AI technology has also entered family. Intelligent computer increase attracting public interest. The emergence of network game, enriching people's life.Some of the main Macintosh and IBM for application software such as voice and character recognition has can buy, Using fuzzy logic,AI technology to simplify the camera equipment. The artificial intelligence technology related to promote greater demand for new progress appear constantly. In a word ，Artificial intelligence has and will continue to inevitably changed our life.附件三英文文献译文人工智能“人工智能”一词最初是在1956 年Dartmouth在学会上提出来的。
智能聊天机器人英语作文500字
智能聊天机器人英语作文500字英文回答：Intelligent Chatbots: A Transformative Influence on Human Communication.Intelligent chatbots, also known as conversational agents, are rapidly revolutionizing the way we interact with technology and communicate with each other. These advanced software programs are designed to simulate human conversation, providing users with personalized and interactive experiences.Chatbots leverage natural language processing (NLP) and machine learning (ML) algorithms to understand user requests and generate appropriate responses. They can engage in text-based or voice-based conversations, mimicking human speech patterns and offering a wide range of functionalities, from answering questions and providing information to completing tasks and offering emotionalsupport.The use of chatbots has proliferated across various industries, including customer service, healthcare, education, and e-commerce. In customer service, chatbots provide 24/7 support, resolving customer queries and addressing their needs promptly and efficiently. In healthcare, chatbots offer health information, trackpatient data, and provide virtual consultations, making healthcare more accessible and convenient. In education, chatbots enhance learning experiences by delivering personalized feedback, answering student questions, and providing interactive exercises. In e-commerce, chatbots help customers find products, navigate websites, and complete purchases, streamlining the shopping process.Despite their numerous benefits, intelligent chatbots also present certain challenges. One concern is the potential for privacy breaches, as chatbots collect and process user data. Another challenge lies in the development of chatbots that can effectively handle complex or ambiguous user requests. Additionally, the ethicalimplications of using chatbots to replace humaninteractions need to be carefully considered.As technology continues to advance, intelligent chatbots will likely become even more sophisticated and deeply integrated into our lives. They have the potential to transform the way we communicate, access information, and interact with the world around us. However, it is important to approach the development and deployment of chatbots with careful consideration, ensuring that they are used for the benefit of humanity and in a responsible manner.中文回答：智能聊天机器人，革新人类沟通方式的变革性力量。
人形机器人中英文对照外文翻译文献
中英文对照翻译最小化传感级别不确定性联合策略的机械手控制摘要：人形机器人的应用应该要求机器人的行为和举止表现得象人。
下面的决定和控制自己在很大程度上的不确定性并存在于获取信息感觉器官的非结构化动态环境中的软件计算方法人一样能想得到。
在机器人领域，关键问题之一是在感官数据中提取有用的知识，然后对信息以及感觉的不确定性划分为各个层次。
本文提出了一种基于广义融合杂交分类（人工神经网络的力量，论坛渔业局）已制定和申请验证的生成合成数据观测模型，以及从实际硬件机器人。
选择这个融合，主要的目标是根据内部（联合传感器）和外部（ Vision 摄像头）感觉信息最大限度地减少不确定性机器人操纵的任务。
目前已被广泛有效的一种方法论就是研究专门配置5个自由度的实验室机器人和模型模拟视觉控制的机械手。
在最近调查的主要不确定性的处理方法包括加权参数选择（几何融合），并指出经过训练在标准操纵机器人控制器的设计的神经网络是无法使用的。
这些方法在混合配置，大大减少了更快和更精确不同级别的机械手控制的不确定性，这中方法已经通过了严格的模拟仿真和试验。
关键词：传感器融合，频分双工，游离脂肪酸，人工神经网络，软计算，机械手，可重复性，准确性，协方差矩阵，不确定性，不确定性椭球。
1 引言各种各样的机器人的应用（工业，军事，科学，医药，社会福利，家庭和娱乐）已涌现了越来越多产品，它们操作范围大并呢那个在非结构化环境中运行 [ 3,12,15]。
在大多数情况下，如何认识环境正在发生变化且每个瞬间最优控制机器人的动作是至关重要的。
移动机器人也基本上都有定位和操作非常大的非结构化的动态环境和处理重大的不确定性的能力[ 1,9,19 ]。
每当机器人操作在随意性自然环境时，在给定的工作将做完的条件下总是存在着某种程度的不确定性。
这些条件可能，有时不同当给定的操作正在执行的时候。
导致这种不确定性的主要的原因是来自机器人的运动参数和各种确定任务信息的差异所引起的。
语音识别参考文献
语音识别参考文献语音识别是一项广泛应用于人机交互、语音翻译、智能助手等领域的技术。
它的目标是将人的语音输入转化为可理解和处理的文本数据。
随着人工智能和机器学习的发展，语音识别技术也得到了极大的提升和应用。
在语音识别领域，有许多经典的参考文献和研究成果。
以下是一些值得参考和研究的文献：1. Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M., Stolcke, A., & Yu, D. (2016). Achieving human parity in conversational speech recognition. arXiv preprintarXiv:1610.05256.这篇文章介绍了微软团队在语音识别方面的研究成果，实现了与人类口语识别准确率相媲美的结果。
2. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., ... & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6), 82-97.这篇文章介绍了深度神经网络在语音识别中的应用和研究进展，对于理解当前主流的语音识别技术有很大的帮助。
3. Hinton, G., Deng, L., Li, D., & Dahl, G. E. (2012). Deep neural networks for speech recognition. IEEE Signal Processing Magazine, 29(6), 82-97.这篇文章是语音识别中的经典之作，介绍了深度神经网络在语音识别中的应用和优势。
扫地机器人语音识别功能作文
扫地机器人语音识别功能作文英文回答：In the realm of household appliances, the evolution of technology has brought forth a new era of convenience and innovation. Robotic vacuum cleaners, once relegated to the sidelines of cleaning routines, have now become indispensable companions in the modern home, offering a blend of efficiency and autonomy. One of the mostremarkable advancements in this field is the integration of voice recognition capabilities into these automated helpers.Voice recognition technology allows robotic vacuum cleaners to respond to commands issued in natural human language. This feature has revolutionized the user experience, enabling seamless interaction between humansand machines. No longer bound by the limitations ofphysical buttons or remote controls, users can now effortlessly direct their robotic vacuum cleaners usingtheir voice.The implementation of voice recognition in robotic vacuum cleaners offers a myriad of benefits. For starters, it enhances accessibility, making these devices more user-friendly for individuals with disabilities or limited mobility. By simply speaking a command, users can effortlessly initiate cleaning cycles, adjust settings, or troubleshoot issues.Moreover, voice recognition technology amplifies the convenience factor of robotic vacuum cleaners. Imagine the scenario: you're engrossed in a riveting movie night, and you notice a stray sock lurking under the couch. Instead of interrupting your entertainment to retrieve the remote control or fumble with buttons on the vacuum cleaner, you can simply utter a command like "Clean under the couch," and the robotic vacuum cleaner will swiftly comply. This hands-free operation allows you to multitask and maintain a spotless home without breaking stride.The integration of voice recognition into robotic vacuum cleaners also paves the way for more personalizedcleaning experiences. By understanding the user's preferences and habits, these devices can tailor their cleaning routines to specific needs. For instance, if you consistently request your vacuum cleaner to focus on high-traffic areas, it can prioritize those zones during subsequent cleaning cycles. This level of customization ensures that your home is cleaned efficiently and in a manner that aligns with your lifestyle.The future of voice recognition technology in robotic vacuum cleaners holds immense promise. As natural language processing continues to advance, we can expect these devices to become even more intelligent and responsive. They may incorporate advanced features such as the ability to recognize multiple users, understand complex commands, and provide real-time feedback on their cleaning progress.In conclusion, the advent of voice recognition in robotic vacuum cleaners has transformed these appliances into indispensable home assistants. By enabling intuitive human-machine interaction, enhanced accessibility, unparalleled convenience, and personalized cleaningexperiences, voice recognition technology empowers users to maintain their homes effortlessly and live smarter, more efficient lives.中文回答：扫地机器人语音识别功能。
基于语音信号的跨语种交互翻译机器人语义纠错方法
基于语音信号的跨语种交互翻译机器人语义纠错方法
付曼
【期刊名称】《信息与电脑》
【年(卷),期】2024(36)5
【摘要】传统的跨语种交互翻译机器人语义纠错方法通常是单向的,效率较低,导致识别错误率较高。
为此,文章提出基于语音信号的跨语种交互翻译机器人语义纠错方法。
在基础语音识别的基础上,通过交互标定和特征提取来修正语义错误位置,并设计语音信号翻译机器人的语义纠错模型,采用随时间反向传播(Backpropagation Through Time,BPTT)循环训练核验方式,以确保纠错的准确性。
测试结果显示,经过3个阶段测试,选定的5段语音材料的纠错识别率成功控制在10%以下,表明基于语音信号的跨语种交互翻译机器人语义纠错方法高效,具有实际应用价值。
【总页数】3页(P31-33)
【作者】付曼
【作者单位】南昌工程学院
【正文语种】中文
【中图分类】TP392
【相关文献】
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2.基于跨语种预训练语言模型XLM-R的神经机器翻译方法
3.基于可靠性的核电站设备维修和寿期管理研
究4.基于语言特征和迁移学习的英语翻译机器人纠错系统研究5.基于神经网络的智能外语翻译机器人语义纠错系统
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机器人外文翻译(中英文翻译)
机器人外文翻译(中英文翻译)机器人外文翻译(中英文翻译)With the rapid development of technology, the use of robots has become increasingly prevalent in various industries. Robots are now commonly employed to perform tasks that are dangerous, repetitive, or require a high level of precision. However, in order for robots to effectively communicate with humans and fulfill their intended functions, accurate translation between different languages is crucial. In this article, we will explore the importance of machine translation in enabling robots to perform translation tasks, as well as discuss current advancements and challenges in this field.1. IntroductionMachine translation refers to the use of computer algorithms to automatically translate text or speech from one language to another. The ultimate goal of machine translation is to produce translations that are as accurate and natural as those generated by human translators. In the context of robots, machine translation plays a vital role in allowing them to understand and respond to human commands, as well as facilitating communication between robots of different origins.2. Advancements in Machine TranslationThe field of machine translation has experienced significant advancements in recent years, thanks to breakthroughs in artificial intelligence and deep learning. These advancements have led to the development of neural machine translation (NMT) systems, which have greatly improved translation quality. NMT models operate by analyzinglarge amounts of bilingual data, allowing them to learn the syntactic and semantic structures of different languages. As a result, NMT systems are capable of providing more accurate translations compared to traditional rule-based or statistical machine translation approaches.3. Challenges in Machine Translation for RobotsAlthough the advancements in machine translation have greatly improved translation quality, there are still challenges that need to be addressed when applying machine translation to robots. One prominent challenge is the variability of language use, including slang, idioms, and cultural references. These nuances can pose difficulties for machine translation systems, as they often require a deep understanding of the context and cultural background. Researchers are currently working on developing techniques to enhance the ability of machine translation systems to handle such linguistic variations.Another challenge is the real-time requirement of translation in a robotic setting. Robots often need to process and translate information on the fly, and any delay in translation can affect the overall performance and efficiency of the robot. Optimizing translation speed without sacrificing translation quality is an ongoing challenge for researchers in the field.4. Applications of Robot TranslationThe ability for robots to translate languages opens up a wide range of applications in various industries. One application is in the field of customer service, where robots can assist customers in multiple languages, providing support and information. Another application is in healthcare settings, where robots can act as interpreters between healthcare professionals and patientswho may speak different languages. Moreover, in international business and diplomacy, robots equipped with translation capabilities can bridge language barriers and facilitate effective communication between parties.5. ConclusionIn conclusion, machine translation plays a crucial role in enabling robots to effectively communicate with humans and fulfill their intended functions. The advancements in neural machine translation have greatly improved translation quality, but challenges such as language variability and real-time translation requirements still exist. With continuous research and innovation, the future of machine translation for robots holds great potential in various industries, revolutionizing the way we communicate and interact with technology.。
ChatGPT：颠覆传统聊天模式的智能聊天机器人(英文中文双语版优质文档)
ChatGPT：颠覆传统聊天模式的智能聊天机器人（英文中文双语版优质文档）ChatGPT is an intelligent chat robot based on deep learning technology. Its appearance subverts the traditional chat mode and brings people a more intelligent and personalized interactive experience.ChatGPT can interact with people in natural language, so as to obtain the needs of users, and provide corresponding services according to the needs of users. Different from traditional chatbots, ChatGPT can not only understand people's language, but also understand people's emotions and context, so that they can more accurately understand users' intentions and provide more considerate services.In addition, ChatGPT can also make personalized recommendations based on user preferences and habits, making interactions more personalized. ChatGPT also supports multilingual interaction, users can interact with ChatGPT in their familiar language, which enables ChatGPT to overcome language barriers in different countries and regions and provide users with global services.ChatGPT also has certain learning and evolution capabilities. It can continuously learn user feedback and behaviors, so as to continuously optimize its own services and improve the quality and efficiency of interactions. This adaptive feature enables ChatGPT to continuously adapt to people's needs and changes, thereby achieving continuous evolution and upgrading.The emergence of ChatGPT has brought people new ideas and new experiences of intelligent interaction. Traditional chatbots can only provide simple services, while ChatGPT can provide users with more intelligent and considerate services, thus bringing more convenience and surprises to people's life and work. It is believed that with the continuous development and progress of technology, the application field of ChatGPT will continue to expand, bringing people a more intelligent, convenient and personalized service experience.ChatGPT 是一款基于深度学习技术的智能聊天机器人，它的出现颠覆了传统的聊天模式，为人们带来了更加智能和个性化的交互体验。
人工智能英文文献原文及译文
人工智能英文文献原文及译文附件四英文文献原文Artificial Intelligence"Artificial intelligence" is a word was originally Dartmouth in 1956 to put forward. From then on, researchers have developed many theories and principles, the concept of artificial intelligence is also expands. Artificial intelligence is a challenging job of science, the person must know computer knowledge, psychology and philosophy. Artificial intelligence is included a wide range of science, it is composed of different fields, such as machine learning, computer vision, etc, on the whole, the research on artificial intelligence is one of the main goals of the machine can do some usually need to perform complex human intelligence. But in different times and different people in the "complex" understanding is different. Such as heavy science and engineering calculation was supposed to be the brain to undertake, now computer can not only complete this calculation, and faster than the human brain can more accurately, and thus the people no longer put this calculation is regarded as "the need to perform complex human intelligence, complex tasks" work is defined as the development of The Times and the progress of technology, artificial intelligence is the science of specific target and nature as The Times change and development. On the one hand it continues to gain new progress on the one hand, and turning to more meaningful, the more difficult the target. Current can be used to study the main material of artificial intelligence and artificial intelligence technology to realize the machine is a computer, the development history of artificial intelligence is computer science and technology and the development together. Besides the computer science and artificial intelligence also involves information, cybernetics, automation, bionics, biology, psychology, logic, linguistics, medicine and philosophy and multi-discipline. Artificial intelligence research include: knowledge representation, automatic reasoning and search method, machine learning and knowledge acquisition and processing of knowledge system, natural language processing, computer vision, intelligent robot, automatic program design, etc.Practical application of machine vision: fingerprint identification,face recognition, retina identification, iris identification, palm, expert system, intelligent identification, search, theorem proving game, automatic programming, and aerospace applications.Artificial intelligence is a subject categories, belong to the door edge discipline of natural science and social science.Involving scientific philosophy and cognitive science, mathematics, neurophysiological, psychology, computer science, information theory, cybernetics, not qualitative theory, bionics.The research category of natural language processing, knowledge representation, intelligent search, reasoning, planning, machine learning, knowledge acquisition, combined scheduling problem, perception, pattern recognition, logic design program, soft calculation, inaccurate and uncertainty, the management of artificial life, neural network, and complex system, human thinking mode of genetic algorithm.Applications of intelligent control, robotics, language and image understanding, genetic programming robot factory.Safety problemsArtificial intelligence is currently in the study, but some scholars think that letting computers have IQ is very dangerous, it may be against humanity. The hidden danger in many movie happened.The definition of artificial intelligenceDefinition of artificial intelligence can be divided into two parts, namely "artificial" or "intelligent". "Artificial" better understanding, also is controversial. Sometimes we will consider what people can make, or people have high degree of intelligence to create artificial intelligence, etc. But generally speaking, "artificial system" is usually significance of artificial system.What is the "smart", with many problems. This involves other such as consciousness, ego, thinking (including the unconscious thoughts etc. People only know of intelligence is one intelligent, this is the universal view of our own. But we are very limited understanding of the intelligence of the intelligent people constitute elements are necessary to find, so it is difficult to define what is "artificial" manufacturing "intelligent". So the artificial intelligence research often involved in the study of intelligent itself. Other about animal or other artificial intelligence system is widely considered to be related to the study of artificial intelligence.Artificial intelligence is currently in the computer field, the moreextensive attention. And in the robot, economic and political decisions, control system, simulation system application. In other areas, it also played an indispensable role.The famous American Stanford university professor nelson artificial intelligence research center of artificial intelligence under such a definition: "artificial intelligence about the knowledge of the subject is and how to represent knowledge -- how to gain knowledge and use of scientific knowledge. But another American MIT professor Winston thought: "artificial intelligence is how to make the computer to do what only can do intelligent work." These comments reflect the artificial intelligence discipline basic ideas and basic content. Namely artificial intelligence is the study of human intelligence activities, has certain law, research of artificial intelligence system, how to make the computer to complete before the intelligence needs to do work, also is to study how the application of computer hardware and software to simulate human some intelligent behavior of the basic theory, methods and techniques.Artificial intelligence is a branch of computer science, since the 1970s, known as one of the three technologies (space technology, energy technology, artificial intelligence). Also considered the 21st century (genetic engineering, nano science, artificial intelligence) is one of the three technologies. It is nearly three years it has been developed rapidly, and in many fields are widely applied, and have made great achievements, artificial intelligence has gradually become an independent branch, both in theory and practice are already becomes a system. Its research results are gradually integrated into people's lives, and create more happiness for mankind.Artificial intelligence is that the computer simulation research of some thinking process and intelligent behavior (such as study, reasoning, thinking, planning, etc.), including computer to realize intelligent principle, make similar to that of human intelligence, computer can achieve higher level of computer application. Artificial intelligence will involve the computer science, philosophy and linguistics, psychology, etc. That was almost natural science and social science disciplines, the scope of all already far beyond the scope of computer science and artificial intelligence and thinking science is the relationship between theory and practice, artificial intelligence is in the mode of thinking science technology application level, is one of its application. From the view of thinking, artificial intelligence is notlimited to logical thinking, want to consider the thinking in image, the inspiration of thought of artificial intelligence can promote the development of the breakthrough, mathematics are often thought of as a variety of basic science, mathematics and language, thought into fields, artificial intelligence subject also must not use mathematical tool, mathematical logic, the fuzzy mathematics in standard etc, mathematics into the scope of artificial intelligence discipline, they will promote each other and develop faster.A brief history of artificial intelligenceArtificial intelligence can be traced back to ancient Egypt's legend, but with 1941, since the development of computer technology has finally can create machine intelligence, "artificial intelligence" is a word in 1956 was first proposed, Dartmouth learned since then, researchers have developed many theories and principles, the concept of artificial intelligence, it expands and not in the long history of the development of artificial intelligence, the slower than expected, but has been in advance, from 40 years ago, now appears to have many AI programs, and they also affected the development of other technologies. The emergence of AI programs, creating immeasurable wealth for the community, promoting the development of human civilization.The computer era1941 an invention that information storage and handling all aspects of the revolution happened. This also appeared in the U.S. and Germany's invention is the first electronic computer. Take a few big pack of air conditioning room, the programmer's nightmare: just run a program for thousands of lines to set the 1949. After improvement can be stored procedure computer programs that make it easier to input, and the development of the theory of computer science, and ultimately computer ai. This in electronic computer processing methods of data, for the invention of artificial intelligence could provide a kind of media.The beginning of AIAlthough the computer AI provides necessary for technical basis, but until the early 1950s, people noticed between machine and human intelligence. Norbert Wiener is the study of the theory of American feedback. Most familiar feedback control example is the thermostat. It will be collected room temperature and hope, and reaction temperature compared to open or close small heater, thus controlling environmental temperature. The importance of the study lies in the feedback loop Wiener:all theoretically the intelligence activities are a result of feedback mechanism and feedback mechanism is. Can use machine. The findings of the simulation of early development of AI.1955, Simon and end Newell called "a logical experts" program. This program is considered by many to be the first AI programs. It will each problem is expressed as a tree, then choose the model may be correct conclusion that a problem to solve. "logic" to the public and the AI expert research field effect makes it AI developing an important milestone in 1956, is considered to be the father of artificial intelligence of John McCarthy organized a society, will be a lot of interest machine intelligence experts and scholars together for a month. He asked them to Vermont Dartmouth in "artificial intelligence research in summer." since then, this area was named "artificial intelligence" although Dartmouth learn not very successful, but it was the founder of the centralized and AI AI research for later laid a foundation.After the meeting of Dartmouth, AI research started seven years. Although the rapid development of field haven't define some of the ideas, meeting has been reconsidered and Carnegie Mellon university. And MIT began to build AI research center is confronted with new challenges. Research needs to establish the: more effective to solve the problem of the system, such as "logic" in reducing search; expert There is the establishment of the system can be self learning.In 1957, "a new program general problem-solving machine" first version was tested. This program is by the same logic "experts" group development. The GPS expanded Wiener feedback principle, can solve many common problem. Two years later, IBM has established a grind investigate group Herbert AI. Gelerneter spent three years to make a geometric theorem of solutions of the program. This achievement was a sensation.When more and more programs, McCarthy busy emerge in the history of an AI. 1958 McCarthy announced his new fruit: LISP until today still LISP language. In. "" mean" LISP list processing ", it quickly adopted for most AI developers.In 1963 MIT from the United States government got a pen is 22millions dollars funding for research funding. The machine auxiliary recognition from the defense advanced research program, have guaranteed in the technological progress on this plan ahead of the Soviet union. Attracted worldwide computer scientists, accelerate the pace of development of AI research.Large programAfter years of program. It appeared a famous called "SHRDLU." SHRDLU "is" the tiny part of the world "project, including the world (for example, only limited quantity of geometrical form of research and programming). In the MIT leadership of Minsky Marvin by researchers found, facing the object, the small computer programs can solve the problem space and logic. Other as in the late 1960's STUDENT", "can solve algebraic problems," SIR "can understand the simple English sentence. These procedures for handling the language understanding and logic.In the 1970s another expert system. An expert system is a intelligent computer program system, and its internal contains a lot of certain areas of experience and knowledge with expert level, can use the human experts' knowledge and methods to solve the problems to deal with this problem domain. That is, the expert system is a specialized knowledge and experience of the program system. Progress is the expert system could predict under certain conditions, the probability of a solution for the computer already has. Great capacity, expert systems possible from the data of expert system. It is widely used in the market. Ten years, expert system used in stock, advance help doctors diagnose diseases, and determine the position of mineral instructions miners. All of this because of expert system of law and information storage capacity and become possible.In the 1970s, a new method was used for many developing, famous as AI Minsky tectonic theory put forward David Marr. Another new theory of machine vision square, for example, how a pair of image by shadow, shape, color, texture and basic information border. Through the analysis of these images distinguish letter, can infer what might be the image in the same period. PROLOGE result is another language, in 1972. In the 1980s, the more rapid progress during the AI, and more to go into business. 1986, the AI related software and hardware sales $4.25 billion dollars. Expert system for its utility, especially by demand. Like digital electric company with such company XCON expert system for the VAX mainframe programming. Dupont, general motors and Boeing has lots of dependence of expert system for computer expert. Some production expert system of manufacture software auxiliary, such as Teknowledge and Intellicorp established. In order to find and correct the mistakes, existing expert system and some other experts system was designed，such as teach users learn TVC expert system of the operating system.From the lab to daily lifePeople began to feel the computer technique and artificial intelligence. No influence of computer technology belong to a group of researchers in the lab. Personal computers and computer technology to numerous technical magazine now before a people. Like the United States artificial intelligence association foundation. Because of the need to develop, AI had a private company researchers into the boom. More than 150 a DEC (it employs more than 700 employees engaged in AI research) that have spent 10 billion dollars in internal AI team.Some other AI areas in the 1980s to enter the market. One is the machine vision Marr and achievements of Minsky. Now use the camera and production, quality control computer. Although still very humble, these systems have been able to distinguish the objects and through the different shape. Until 1985 America has more than 100 companies producing machine vision systems, sales were us $8 million.But the 1980s to AI and industrial all is not a good year for years. 1986-87 AI system requirements, the loss of industry nearly five hundred million dollars. Teknowledge like Intellicorp and two loss of more than $6 million, about one-third of the profits of the huge losses forced many research funding cuts the guide led. Another disappointing is the defense advanced research programme support of so-called "intelligent" this project truck purpose is to develop a can finish the task in many battlefield robot. Since the defects and successful hopeless, Pentagon stopped project funding.Despite these setbacks, AI is still in development of new technology slowly. In Japan were developed in the United States, such as the fuzzy logic, it can never determine the conditions of decision making, And neural network, regarded as the possible approaches to realizing artificial intelligence. Anyhow, the eighties was introduced into the market, the AI and shows the practical value. Sure, it will be the key to the 21st century. "artificial intelligence technology acceptance inspection in desert storm" action of military intelligence test equipment through war. Artificial intelligence technology is used to display the missile system and warning and other advanced weapons. AI technology has also entered family. Intelligent computer increase attracting public interest. The emergence of network game, enriching people's life.Some of the main Macintosh and IBM for application software such as voice and character recognition has can buy, Using fuzzy logic,AI technology to simplify the camera equipment. The artificial intelligence technology related to promote greater demand for new progress appear constantly. In a word ，Artificial intelligence has and will continue to inevitably changed our life.附件三英文文献译文人工智能“人工智能”一词最初是在1956 年Dartmouth在学会上提出来的。
机器人技术发展趋势论文中英文对照资料外文翻译文献
中英文对照资料外文翻译文献机器人技术发展趋势谈到机器人，现实仍落后于科幻小说。
但是，仅仅因为机器人在过去的几十年没有实现它们的承诺，并不意味着机器人的时代不会到来，或早或晚。
事实上，多种先进技术的影响已经使得机器人的时代变得更近——更小、更便宜、更实用和更具成本效益。
肌肉、骨骼和大脑任何一个机器人都有三方面：·肌肉——有效联系有关物理荷载以便于机器人运动。
·骨骼——一个机器人的物理结构取决于它所做的工作；它的尺寸大小和重量则取决于它的物理荷载。
·大脑——机器人智能；它能独立思考和做什么；需要多少人工互动。
由于机器人在科幻世界中所被描绘过的方式，很多人希望机器人在外型上与人类相似。
但事实上，机器人的外形更多地取决于它所做的工作或具备的功能。
很多一点儿也不像人的机器也被清楚地归为机器人。
同样，很多看起来像人的机器却还是仅仅属于机械结构和玩具。
很多早期的机器人是除了有很大力气而毫无其他功能的大型机器。
老式的液压动力机器人已经被用来执行3-D任务即平淡、肮脏和危险的任务。
由于第一产业技术的进步，完全彻底地改进了机器人的性能、业绩和战略利益。
比如，20世纪80年代，机器人开始从液压动力转换成为电动单位。
精度和性能也提高了。
工业机器人已经在工作时至今日，全世界机器人的数量已经接近100万，其中超过半数的机器人在日本，而仅仅只有15%在美国。
几十年前，90%的机器人是服务于汽车生产行业，通常用于做大量重复的工作。
现在，只有50%的机器人用于汽车制造业，而另一半分布于工厂、实验室、仓库、发电站、医院和其他的行业。
机器人用于产品装配、危险物品处理、油漆喷雾、抛光、产品的检验。
用于清洗下水道，探测炸弹和执行复杂手术的各种任务的机器人数量正在稳步增加，在未来几年内将继续增长。
机器人智能即使是原始的智力，机器人已经被证明了在生产力、效率和质量方面都能够创造良好的效益。
除此之外，一些“最聪明的”机器人没有用于制造业；它们被用于太空探险、外科手术遥控，甚至于宠物，比如索尼的AIBO电子狗。
机器人外文翻译(中英文翻译)
外文翻译机器人The robot性质: □毕业设计□毕业论文教学院：机电工程学院系别：机械设计制造及其自动化学生学号：学生姓名：专业班级：指导教师：职称：起止日期：机器人1.机器人的作用机器人是高级整合控制论、机械电子、计算机、材料和仿生学的产物。
在工业、医学、农业、建筑业甚至军事等领域中均有重要用途。
现在，国际上对机器人的概念已经逐渐趋近一致。
一般说来，人们都可以接受这种说法，即机器人是靠自身动力和控制能力来实现各种功能的一种机器。
联合国标准化组织采纳了美国机器人协会给机器人下的定义：“一种可编程和多功能的，用来搬运材料、零件、工具的操作机；或是为了执行不同的任务而具有可改变和可编程动作的专门系统。
2.能力评价标准机器人能力的评价标准包括：智能，指感觉和感知，包括记忆、运算、比较、鉴别、判断、决策、学习和逻辑推理等；机能，指变通性、通用性或空间占有性等；物理能，指力、速度、连续运行能力、可靠性、联用性、寿命等。
因此，可以说机器人是具有生物功能的三维空间坐标机器。
3.机器人的组成机器人一般由执行机构、驱动装置、检测装置和控制系统等组成。
执行机构即机器人本体，其臂部一般采用空间开链连杆机构，其中的运动副（转动副或移动副）常称为关节，关节个数通常即为机器人的自由度数。
根据关节配置型式和运动坐标形式的不同，机器人执行机构可分为直角坐标式、圆柱坐标式、极坐标式和关节坐标式等类型。
出于拟人化的考虑，常将机器人本体的有关部位分别称为基座、腰部、臂部、腕部、手部（夹持器或末端执行器）和行走部（对于移动机器人）等。
驱动装置是驱使执行机构运动的机构，按照控制系统发出的指令信号，借助于动力元件使机器人进行动作。
它输入的是电信号，输出的是线、角位移量。
机器人使用的驱动装置主要是电力驱动装置，如步进电机、伺服电机等，此外也有采用液压、气动等驱动装置。
检测装置的作用是实时检测机器人的运动及工作情况，根据需要反馈给控制系统，与设定信息进行比较后，对执行机构进行调整，以保证机器人的动作符合预定的要求。
工业机器人课设参考文献
工业机器人课设参考文献工业机器人课设参考文献引言：工业机器人是现代生产制造领域中的重要一环，其在提高生产效率、降低劳动强度和提升产品质量方面发挥着关键作用。
在工业机器人的设计和应用过程中，课设作为一种实践性的学习任务，可以帮助学生更好地理解和应用相关知识。
本篇文章将为你提供一些工业机器人课设方面的参考文献，以供你参考和借鉴。
一、工业机器人概述：1. Woodson, W. E., & Schott, R. J. (2019). Introduction to Robotics. New York, NY: Springer.该书详细介绍了机器人的发展历程、机器人技术的基本原理以及机器人系统的组成部分。
它还提供了广泛的实例以帮助读者理解机器人在各个领域的应用。
2. Asfahl, C. R. (2016). Industrial Robotics: Theory, Modelling and Control. Hoboken, NJ: Wiley.该书探讨了工业机器人的理论基础、建模方法和控制策略。
它详细介绍了机器人运动学、动力学、传感器和执行器等相关知识，对于设计和控制工业机器人系统非常有帮助。
二、工业机器人应用：1. Khatib, O. (2016). Springer Handbook of Robotics. New York, NY: Springer.这本手册涵盖了机器人学的广泛领域，包括工业机器人的应用。
其中的一些章节特别涉及到了工业机器人在自动化生产、装配、焊接、包装等方面的应用。
2. Siciliano, B., & Khatib, O. (2008). Springer Handbook of Robotics. New York, NY: Springer.该手册包含了工业机器人在制造业中的应用和挑战。
其中的章节涵盖了机器人视觉、语音识别和智能控制等方面的技术，为理解和应用机器人在工业环境中的任务提供了重要参考。
5-自动化 外文文献 英文文献 外文翻译 改进型智能机器人的语音识别方法
附件1：外文资料翻译译文改进型智能机器人的语音识别方法2、语音识别概述最近，由于其重大的理论意义和实用价值，语音识别已经受到越来越多的关注。
到现在为止，多数的语音识别是基于传统的线性系统理论，例如隐马尔可夫模型和动态时间规整技术。
随着语音识别的深度研究，研究者发现，语音信号是一个复杂的非线性过程，如果语音识别研究想要获得突破，那么就必须引进非线性系统理论方法。
最近，随着非线性系统理论的发展，如人工神经网络，混沌与分形，可能应用这些理论到语音识别中。
因此，本文的研究是在神经网络和混沌与分形理论的基础上介绍了语音识别的过程。
语音识别可以划分为独立发声式和非独立发声式两种。
非独立发声式是指发音模式是由单个人来进行训练，其对训练人命令的识别速度很快，但它对与其他人的指令识别速度很慢，或者不能识别。
独立发声式是指其发音模式是由不同年龄，不同性别，不同地域的人来进行训练，它能识别一个群体的指令。
一般地，由于用户不需要操作训练，独立发声式系统得到了更广泛的应用。
所以，在独立发声式系统中，从语音信号中提取语音特征是语音识别系统的一个基本问题。
语音识别包括训练和识别，我们可以把它看做一种模式化的识别任务。
通常地，语音信号可以看作为一段通过隐马尔可夫模型来表征的时间序列。
通过这些特征提取，语音信号被转化为特征向量并把它作为一种意见，在训练程序中，这些意见将反馈到HMM的模型参数估计中。
这些参数包括意见和他们响应状态所对应的概率密度函数，状态间的转移概率，等等。
经过参数估计以后，这个已训练模式就可以应用到识别任务当中。
输入信号将会被确认为造成词，其精确度是可以评估的。
整个过程如图一所示。
图1 语音识别系统的模块图3、理论与方法从语音信号中进行独立扬声器的特征提取是语音识别系统中的一个基本问题。
解决这个问题的最流行方法是应用线性预测倒谱系数和Mel频率倒谱系数。
这两种方法都是基于一种假设的线形程序，该假设认为说话者所拥有的语音特性是由于声道共振造成的。
语音识别外文翻译外文文献英文文献
Speech RecognitionVictor Zue, Ron Cole, & Wayne WardMIT Laboratory for Computer Science, Cambridge, Massachusetts, USA Oregon Graduate Institute of Science & Technology, Portland, Oregon, USACarnegie Mellon University, Pittsburgh, Pennsylvania, USA1 Defining the ProblemSpeech recognition is the process of converting an acoustic signal, captured by a microphone or a telephone, to a set of words. The recognized words can be the final results, as for applications such as commands & control, data entry, and document preparation. They can also serve as the input to further linguistic processing in order to achieve speech understanding, a subject covered in section.Speech recognition systems can be characterized by many parameters, some of the more important of which are shown in Figure. An isolated-word speech recognition system requires that the speaker pause briefly between words, whereas a continuous speech recognition system does not. Spontaneous, or extemporaneously generated, speech contains disfluencies, and is much more difficult to recognize than speech read from script. Some systems require speaker enrollment---a user must provide samples of his or her speech before using them, whereas other systems are said to be speaker-independent, in that no enrollment is necessary. Some of the other parameters depend on the specific task. Recognition is generally more difficult when vocabularies are large or have many similar-sounding words. When speech is produced in a sequence of words, language models or artificial grammars are used to restrict the combination of words.The simplest language model can be specified as a finite-state network, where the permissible words following each word are given explicitly. More general language models approximating natural language are specified in terms of a context-sensitive grammar.One popular measure of the difficulty of the task, combining the vocabulary size and the 1 language model, is perplexity, loosely defined as the geometric mean of the number of words that can follow a word after the language model has been applied (see section for a discussion of language modeling in general and perplexity in particular). Finally, there are some external parameters that can affect speech recognition system performance, including the characteristics of the environmental noise and the type and the placement of the microphone.Speech recognition is a difficult problem, largely because of the many sources of variability associated with the signal. First, the acoustic realizations of phonemes, the smallest sound units of which words are composed, are highly dependent on the context in which they appear. These phonetic variabilities are exemplified by the acoustic differences of the phoneme，At word boundaries, contextual variations can be quite dramatic---making gas shortage sound like gash shortage in American English, and devo andare sound like devandare in Italian.Second, acoustic variabilities can result from changes in the environment as well as in the position and characteristics of the transducer. Third, within-speaker variabilities can result from changes in the speaker's physical and emotional state, speaking rate, or voice quality. Finally, differences in sociolinguistic background, dialect, and vocal tract size and shape can contribute to across-speaker variabilities.Figure shows the major components of a typical speech recognition system. The digitized speech signal is first transformed into a set of useful measurements or features at a fixed rate, 2 typically once every 10--20 msec (see sectionsand 11.3 for signal representation and digital signal processing, respectively). These measurements are then used to search for the most likely word candidate, making use of constraints imposed by the acoustic, lexical, and language models. Throughout this process, training data are used to determine the values of the model parameters.Speech recognition systems attempt to model the sources of variability described above in several ways. At the level of signal representation, researchers have developed representations that emphasize perceptually important speaker-independent features of the signal, and de-emphasize speaker-dependent characteristics. At theacoustic phonetic level, speaker variability is typically modeled using statistical techniques applied to large amounts of data. Speaker adaptation algorithms have also been developed that adapt speaker-independent acoustic models to those of the current speaker during system use, (see section). Effects of linguistic context at the acoustic phonetic level are typically handled by training separate models for phonemes in different contexts; this is called context dependent acoustic modeling.Word level variability can be handled by allowing alternate pronunciations of words in representations known as pronunciation networks. Common alternate pronunciations of words, as well as effects of dialect and accent are handled by allowing search algorithms to find alternate paths of phonemes through these networks. Statistical language models, based on estimates of the frequency of occurrence of word sequences, are often used to guide the search through the most probable sequence of words.The dominant recognition paradigm in the past fifteen years is known as hidden Markov models (HMM). An HMM is a doubly stochastic model, in which the generation of the underlying phoneme string and the frame-by-frame, surface acoustic realizations are both represented probabilistically as Markov processes, as discussed in sections,and 11.2. Neural networks have also been used to estimate the frame based scores; these scores are then integrated into HMM-based system architectures, in what has come to be known as hybrid systems, as described in section 11.5.An interesting feature of frame-based HMM systems is that speech segments are identified during the search process, rather than explicitly. An alternate approach is to first identify speech segments, then classify the segments and use the segment scores to recognize words. This approach has produced competitive recognition performance in several tasks.2 State of the ArtComments about the state-of-the-art need to be made in the context of specific applications which reflect the constraints on the task. Moreover, different technologies are sometimes appropriate for different tasks. For example, when the vocabulary issmall, the entire word can be modeled as a single unit. Such an approach is not practical for large vocabularies, where word models must be built up from subword units.The past decade has witnessed significant progress in speech recognition technology. Word error rates continue to drop by a factor of 2 every two years. Substantial progress has been made in the basic technology, leading to the lowering of barriers to speaker independence, continuous speech, and large vocabularies. There are several factors that have contributed to this rapid progress. First, there is the coming of age of the HMM. HMM is powerful in that, with the availability of training data, the parameters of the model can be trained automatically to give optimal performance.Second, much effort has gone into the development of large speech corpora for system development, training, and testing. Some of these corpora are designed for acoustic phonetic research, while others are highly task specific. Nowadays, it is not uncommon to have tens of thousands of sentences available for system training and testing. These corpora permit researchers to quantify the acoustic cues important for phonetic contrasts and to determine parameters of the recognizers in a statistically meaningful way. While many of these corpora (e.g., TIMIT, RM, A TIS, and WSJ; see section 12.3) were originally collected under the sponsorship of the U.S. Defense Advanced Research Projects Agency (ARPA) to spur human language technology development among its contractors, they have nevertheless gained world-wide acceptance (e.g., in Canada, France, Germany, Japan, and the U.K.) as standards on which to evaluate speech recognition.Third, progress has been brought about by the establishment of standards for performance evaluation. Only a decade ago, researchers trained and tested their systems using locally collected data, and had not been very careful in delineating training and testing sets. As a result, it was very difficult to compare performance across systems, and a system's performance typically degraded when it was presented with previously unseen data. The recent availability of a large body of data in the public domain, coupled with the specification of evaluation standards, has resulted inuniform documentation of test results, thus contributing to greater reliability in monitoring progress (corpus development activities and evaluation methodologies are summarized in chapters 12 and 13 respectively).Finally, advances in computer technology have also indirectly influenced our progress. The availability of fast computers with inexpensive mass storage capabilities has enabled researchers to run many large scale experiments in a short amount of time. This means that the elapsed time between an idea and its implementation and evaluation is greatly reduced. In fact, speech recognition systems with reasonable performance can now run in real time using high-end workstations without additional hardware---a feat unimaginable only a few years ago.One of the most popular, and potentially most useful tasks with low perplexity (PP=11) is the recognition of digits. For American English, speaker-independent recognition of digit strings spoken continuously and restricted to telephone bandwidth can achieve an error rate of 0.3% when the string length is known.One of the best known moderate-perplexity tasks is the 1,000-word so-called Resource 5 Management (RM) task, in which inquiries can be made concerning various naval vessels in the Pacific ocean. The best speaker-independent performance on the RM task is less than 4%, using a word-pair language model that constrains the possible words following a given word (PP=60). More recently, researchers have begun to address the issue of recognizing spontaneously generated speech. For example, in the Air Travel Information Service (ATIS) domain, word error rates of less than 3% has been reported for a vocabulary of nearly 2,000 words and a bigram language model with a perplexity of around 15.High perplexity tasks with a vocabulary of thousands of words are intended primarily for the dictation application. After working on isolated-word, speaker-dependent systems for many years, the community has since 1992 moved towards very-large-vocabulary (20,000 words and more), high-perplexity (PP≈200), speaker-independent, continuous speech recognition. The best system in 1994 achieved an error rate of 7.2% on read sentences drawn from North America business news.With the steady improvements in speech recognition performance, systems are now being deployed within telephone and cellular networks in many countries. Within the next few years, speech recognition will be pervasive in telephone networks around the world. There are tremendous forces driving the development of the technology; in many countries, touch tone penetration is low, and voice is the only option for controlling automated services. In voice dialing, for example, users can dial 10--20 telephone numbers by voice (e.g., call home) after having enrolled their voices by saying the words associated with telephone numbers. AT&T, on the other hand, has installed a call routing system using speaker-independent word-spotting technology that can detect a few key phrases (e.g., person to person, calling card) in sentences such as: I want to charge it to my calling card.At present, several very large vocabulary dictation systems are available for document generation. These systems generally require speakers to pause between words. Their performance can be further enhanced if one can apply constraints of the specific domain such as dictating medical reports.Even though much progress is being made, machines are a long way from recognizing conversational speech. Word recognition rates on telephone conversations in the Switchboard corpus are around 50%. It will be many years before unlimited vocabulary, speaker-independent continuous dictation capability is realized.3 Future DirectionsIn 1992, the U.S. National Science Foundation sponsored a workshop to identify the key research challenges in the area of human language technology, and the infrastructure needed to support the work. The key research challenges are summarized in. Research in the following areas for speech recognition were identified:Robustness:In a robust system, performance degrades gracefully (rather than catastrophically) as conditions become more different from those under which it was trained. Differences in channel characteristics and acoustic environment shouldreceive particular attention.Portability:Portability refers to the goal of rapidly designing, developing and deploying systems for new applications. At present, systems tend to suffer significant degradation when moved to a new task. In order to return to peak performance, they must be trained on examples specific to the new task, which is time consuming and expensive.Adaptation:How can systems continuously adapt to changing conditions (new speakers, microphone, task, etc) and improve through use? Such adaptation can occur at many levels in systems, subword models, word pronunciations, language models, etc.Language Modeling:Current systems use statistical language models to help reduce the search space and resolve acoustic ambiguity. As vocabulary size grows and other constraints are relaxed to create more habitable systems, it will be increasingly important to get as much constraint as possible from language models; perhaps incorporating syntactic and semantic constraints that cannot be captured by purely statistical models.Confidence Measures:Most speech recognition systems assign scores to hypotheses for the purpose of rank ordering them. These scores do not provide a good indication of whether a hypothesis is correct or not, just that it is better than the other hypotheses. As we move to tasks that require actions, we need better methods to evaluate the absolute correctness of hypotheses.Out-of-Vocabulary W ords:Systems are designed for use with a particular set of words, but system users may not know exactly which words are in the system vocabulary. This leads to a certain percentage of out-of-vocabulary words in natural conditions. Systems must have some method of detecting such out-of-vocabulary words, or they will end up mapping a word from the vocabulary onto the unknown word, causing an error.Spontaneous Speech:Systems that are deployed for real use must deal with a variety of spontaneous speech phenomena, such as filled pauses, false starts, hesitations, ungrammatical constructions and other common behaviors not found in read speech. Development on the ATIS task has resulted in progress in this area, but much work remains to be done.Prosody:Prosody refers to acoustic structure that extends over several segments or words. Stress, intonation, and rhythm convey important information for word recognition and the user's intentions (e.g., sarcasm, anger). Current systems do not capture prosodic structure. How to integrate prosodic information into the recognition architecture is a critical question that has not yet been answered.Modeling Dynamics:Systems assume a sequence of input frames which are treated as if they were independent. But it is known that perceptual cues for words and phonemes require the integration of features that reflect the movements of the articulators, which are dynamic in nature. How to model dynamics and incorporate this information into recognition systems is an unsolved problem.语音识别舒维都，罗恩科尔，韦恩沃德麻省理工学院计算机科学实验室，剑桥，马萨诸塞州，美国俄勒冈科学与技术学院，波特兰，俄勒冈州，美国卡耐基梅隆大学，匹兹堡，宾夕法尼亚州，美国一定义问题语音识别是指音频信号的转换过程，被电话或麦克风的所捕获的一系列的消息。
玩具机器人英文作文带翻译
玩具机器人英文作文带翻译英文，When it comes to toy robots, I have to say that they have always been one of my favorite toys since I was a child. I remember receiving my first toy robot as abirthday present when I was 7 years old. It was a small,red robot that could walk, talk, and even dance. I was so excited and couldn't wait to play with it. From that moment on, I became obsessed with toy robots and collected many different kinds.One of the things I love about toy robots is theirability to mimic human movements and behaviors. For example, I have a toy robot that can transform into a car and back into a robot. It's amazing to see how it can change its shape and move around just like a real car. Another robot I have is equipped with voice recognition technology, so it can respond to my commands and hold simple conversationswith me. It's like having a little friend to talk to whenever I want.Not only are toy robots fun to play with, but they also have educational benefits. For instance, I have a programmable robot that allows me to learn basic coding and programming skills. I can create different commands for the robot to follow, such as moving in a specific pattern or making sounds at certain times. It's a great way for me to develop problem-solving abilities and logical thinking.In addition, toy robots have become more advanced in recent years with the integration of artificialintelligence. I have a robot that can recognize my face and greet me when I come home. It can also learn my preferences and adjust its behavior accordingly. It's fascinating tosee how technology has enhanced the capabilities of toy robots and made them more interactive and responsive.Overall, toy robots have brought me so much joy and entertainment over the years. They have not only provided me with hours of fun, but also helped me develop various skills. I believe that toy robots will continue to evolve and become even more sophisticated in the future, and Ican't wait to see what amazing features they will have next.中文，说到玩具机器人，我必须说它们一直是我从小就喜欢的玩具之一。
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中英文资料外文翻译译文:改进型智能机器人的语音识别方法2、语音识别概述最近，由于其重大的理论意义和实用价值，语音识别已经受到越来越多的关注。
到现在为止，多数的语音识别是基于传统的线性系统理论，例如隐马尔可夫模型和动态时间规整技术。
随着语音识别的深度研究，研究者发现，语音信号是一个复杂的非线性过程，如果语音识别研究想要获得突破，那么就必须引进非线性系统理论方法。
最近，随着非线性系统理论的发展，如人工神经网络，混沌与分形，可能应用这些理论到语音识别中。
因此，本文的研究是在神经网络和混沌与分形理论的基础上介绍了语音识别的过程。
语音识别可以划分为独立发声式和非独立发声式两种。
非独立发声式是指发音模式是由单个人来进行训练，其对训练人命令的识别速度很快，但它对与其他人的指令识别速度很慢，或者不能识别。
独立发声式是指其发音模式是由不同年龄，不同性别，不同地域的人来进行训练，它能识别一个群体的指令。
一般地，由于用户不需要操作训练，独立发声式系统得到了更广泛的应用。
所以，在独立发声式系统中，从语音信号中提取语音特征是语音识别系统的一个基本问题。
语音识别包括训练和识别，我们可以把它看做一种模式化的识别任务。
通常地，语音信号可以看作为一段通过隐马尔可夫模型来表征的时间序列。
通过这些特征提取，语音信号被转化为特征向量并把它作为一种意见，在训练程序中，这些意见将反馈到HMM的模型参数估计中。
这些参数包括意见和他们响应状态所对应的概率密度函数，状态间的转移概率，等等。
经过参数估计以后，这个已训练模式就可以应用到识别任务当中。
输入信号将会被确认为造成词，其精确度是可以评估的。
整个过程如图一所示。
图1 语音识别系统的模块图3、理论与方法从语音信号中进行独立扬声器的特征提取是语音识别系统中的一个基本问题。
解决这个问题的最流行方法是应用线性预测倒谱系数和Mel频率倒谱系数。
这两种方法都是基于一种假设的线形程序，该假设认为说话者所拥有的语音特性是由于声道共振造成的。
这些信号特征构成了语音信号最基本的光谱结构。
然而，在语音信号中，这些非线形信息不容易被当前的特征提取逻辑方法所提取，所以我们使用分型维数来测量非线形语音扰动。
本文利用传统的LPCC和非线性多尺度分形维数特征提取研究并实现语音识别系统。
3.1线性预测倒谱系数线性预测系数是一个我们在做语音的线形预分析时得到的参数，它是关于毗邻语音样本间特征联系的参数。
线形预分析正式基于以下几个概念建立起来的，即一个语音样本可以通过一些以前的样本的线形组合来快速地估计，根据真实语音样本在确切的分析框架（短时间内的）和预测样本之间的差别的最小平方原则，最后会确认出唯一的一组预测系数。
LPC可以用来估计语音信号的倒谱。
在语音信号的短时倒谱分析中，这是一种特殊的处理方法。
信道模型的系统函数可以通过如下的线形预分析来得到：其中p代表线形预测命令，，（k=1，2，… …，p）代表预测参数，脉冲响应用h(n)来表示，假设h（n）的倒谱是。
那么（1）式可以扩展为（2）式：将（1）带入（2），两边同时，（2）变成（3）。
就获得了方程（4）：那么可以通过来获得。
（5）中计算的倒谱系数叫做LPCC，n代表LPCC命令。
在我们采集LPCC参数以前，我们应该对语音信号进行预加重，帧处理，加工和终端窗口检测等，所以，中文命令字“前进”的端点检测如图2所示，接下来，断点检测后的中文命令字“前进”语音波形和LPCC的参数波形如图3所示。
图2 中文命令字“前进”的端点检测图3 断点检测后的中文命令字“前进”语音波形和LPCC的参数波形3.2 语音分形维数计算分形维数是一个与分形的规模与数量相关的定值，也是对自我的结构相似性的测量。
分形分维测量是[6-7]。
从测量的角度来看，分形维数从整数扩展到了分数，打破了一般集拓扑学方面被整数分形维数的限制，分数大多是在欧几里得几何尺寸的延伸。
有许多关于分形维数的定义，例如相似维度，豪斯多夫维度，信息维度，相关维度，容积维度，计盒维度等等，其中，豪斯多夫维度是最古老同时也是最重要的，它的定义如【3】所示：其中，表示需要多少个单位来覆盖子集F.端点检测后，中文命令词“向前”的语音波形和分形维数波形如图4所示。
图4 端点检测后，中文命令词“向前”的语音波形和分形维数波形3.3 改进的特征提取方法考虑到LPCC语音信号和分形维数在表达上各自的优点，我们把它们二者混合到信号的特取中，即分形维数表表征语音时间波形图的自相似性，周期性，随机性，同时，LPCC特性在高语音质量和高识别速度上做得很好。
由于人工神经网络的非线性，自适应性，强大的自学能力这些明显的优点，它的优良分类和输入输出响应能力都使它非常适合解决语音识别问题。
由于人工神经网络的输入码的数量是固定的，因此，现在是进行正规化的特征参数输入到前神经网络[9]，在我们的实验中，LPCC和每个样本的分形维数需要分别地通过时间规整化的网络，LPCC是一个4帧数据（LPCC1,LPCC2,LPCC3,LPCC4，每个参数都是14维的），分形维数被模范化为12维数据，（FD1,FD2,…FD12，每一个参数都是一维），以便于每个样本的特征向量有4*14+12*1=68-D维，该命令就是前56个维数是LPCC，剩下的12个维数是分形维数。
因而，这样的一个特征向量可以表征语音信号的线形和非线性特征。
自动语音识别的结构和特征自动语音识别是一项尖端技术，它允许一台计算机，甚至是一台手持掌上电脑（迈尔斯，2000）来识别那些需要朗读或者任何录音设备发音的词汇。
自动语音识别技术的最终目的是让那些不论词汇量，背景噪音，说话者变音的人直白地说出的单词能够达到100%的准确率（CSLU，2002）。
然而，大多数的自动语音识别工程师都承认这样一个现状，即对于一个大的语音词汇单位，当前的准确度水平仍然低于90%。
举一个例子，Dragon's Naturally Speaking或者IBM公司，阐述了取决于口音，背景噪音，说话方式的基线识别的准确性仅仅为60%至80%(Ehsani & Knodt, 1998)。
更多的能超越以上两个的昂贵的系统有Subarashii (Bernstein, et al., 1999), EduSpeak (Franco, etal., 2001), Phonepass (Hinks, 2001), ISLE Project (Menzel, et al., 2001) and RAD (CSLU, 2003)。
语音识别的准确性将有望改善。
在自动语音识别产品中的几种语音识别方式中，隐马尔可夫模型（HMM）被认为是最主要的算法，并且被证明在处理大词汇语音时是最高效的(Ehsani & Knodt, 1998)。
详细说明隐马尔可夫模型如何工作超出了本文的范围，但可以在任何关于语言处理的文章中找到。
其中最好的是Jurafsky & Martin (2000) and Hosom, Cole, and Fanty (2003)。
简而言之，隐马尔可夫模型计算输入接收信号和包含于一个拥有数以百计的本土音素录音的数据库的匹配可能性(Hinks, 2003, p. 5)。
也就是说，一台基于隐马尔可夫模型的语音识别器可以计算输入一个发音的音素可以和一个基于概率论相应的模型达到的达到的接近度。
高性能就意味着优良的发音，低性能就意味着劣质的发音(Larocca, et al., 1991)。
虽然语音识别已被普遍用于商业听写和获取特殊需要等目的，近年来，语言学习的市场占有率急剧增加(Aist, 1999; Eskenazi, 1999; Hinks, 2003)。
早期的基于自动语音识别的软件程序采用基于模板的识别系统，其使用动态规划执行模式匹配或其他时间规范化技术(Dalby & Kewley-Port,1999). 这些程序包括Talk to Me (Auralog, 1995), the Tell Me More Series (Auralog, 2000), Triple-Play Plus (Mackey & Choi, 1998), New Dynamic English (DynEd, 1997), English Discoveries (Edusoft, 1998), and See it, Hear It, SAY IT! (CPI, 1997)。
这些程序的大多数都不会提供任何反馈给超出简单说明的发音准确率，这个基于最接近模式匹配说明是由用户提出书面对话选择的。
学习者不会被告之他们发音的准确率。
特别是内里，（2002年）评论例如Talk to Me和Tell Me More等作品中的波形图，因为他们期待浮华的买家，而不会提供有意义的反馈给用户。
Talk to Me 2002年的版本已经包含了更多Hinks (2003)的特性，比如，信任对于学习者来说是非常有用的:★一个视觉信号可以让学习者把他们的语调同模型扬声器发出的语调进行对比。
★学习者发音的准确度通常以数字7来度量（越高越好）★那些发音失真的词语会被识别出来并被明显地标注。
原文:Improved speech recognition methodfor intelligent robot2、Overview of speech recognitionSpeech recognition has received more and more attention recently due to the important theoretical meaning and practical value [5 ]. Up to now, most speech recognition is based on conventional linear system theory, such as Hidden Markov Model (HMM) and Dynamic Time Warping(DTW) . With the deep study of speech recognition, it is found that speech signal is a complex nonlinear process. If the study of speech recognition wants to break through, nonlinear-system theory method must be introduced to it. Recently, with the developmentof nonlinea-system theories such as artificial neural networks(ANN) , chaos and fractal, it is possible to apply these theories to speech recognition. Therefore, the study of this paper is based on ANN and chaos and fractal theories are introduced to process speech recognition.Speech recognition is divided into two ways that are speaker dependent and speaker independent. Speaker dependent refers to the pronunciation model trained by a single person, the identification rate of the training person?sorders is high, while others’orders is in low identification rate or can’t be recognized. Speaker independent refers to the pronunciation modeltrained by persons of different age, sex and region, it can identify a group of persons’orders. Generally,speaker independent system ismorewidely used, since the user is not required to conduct the training. So extraction of speaker independent features from the speech signal is the fundamental problem of speaker recognition system.Speech recognition can be viewed as a pattern recognition task, which includes training and recognition.Generally, speech signal can be viewed as a time sequence and characterized by the powerful hidden Markov model (HMM). Through the feature extraction, the speech signal is transferred into feature vectors and act asobservations. In the training procedure, these observationswill feed to estimate the model parameters of HMM. These parameters include probability density function for the observations and their corresponding states, transition probability between the states, etc. After the parameter estimation, the trained models can be used for recognition task. The input observations will be recognized as the resulted words and the accuracy can be evaluated. Thewhole process is illustrated in Fig. 1.Fig. 1Block diagram of speech recognition system3 Theory andmethodExtraction of speaker independent features from the speech signal is the fundamental problem of speaker recognition system. The standard methodology for solving this problem uses Linear Predictive Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Co-efficient (MFCC). Both these methods are linear procedures based on the assumption that speaker features have properties caused by the vocal tract resonances. These features form the basic spectral structure of the speech signal. However, the non-linear information in speech signals is not easily extracted by the present feature extraction methodologies. So we use fractal dimension to measure non2linear speech turbulence.This paper investigates and implements speaker identification system using both traditional LPCC and non-linear multiscaled fractal dimension feature extraction.3. 1L inear Predictive Cepstral CoefficientsLinear prediction coefficient (LPC) is a parameter setwhich is obtained when we do linear prediction analysis of speech. It is about some correlation characteristics between adjacent speech samples. Linear prediction analysis is based on the following basic concepts. That is, a speech sample can be estimated approximately by the linear combination of some past speech samples. According to the minimal square sum principle of difference between real speech sample in certain analysis frameshort-time and predictive sample, the only group ofprediction coefficients can be determined.LPC coefficient can be used to estimate speech signal cepstrum. This is a special processing method in analysis of speech signal short-time cepstrum. System function of channelmodel is obtained by linear prediction analysis as follow.Where p represents linear prediction order, ak,(k=1,2,…,p) represent sprediction coefficient, Impulse response is represented by h(n). Supposecepstrum of h(n) is represented by ,then (1) can be expanded as (2).The cepstrum coefficient calculated in the way of (5) is called LPCC, n represents LPCC order.When we extract LPCC parameter before, we should carry on speech signal pre-emphasis, framing processing, windowingprocessing and endpoints detection etc. , so the endpoint detection of Chinese command word“Forward”is shown in Fig.2, next, the speech waveform ofChinese command word“Forward”and LPCC parameter waveform after Endpoint detection is shown in Fig. 3.3. 2 Speech Fractal Dimension ComputationFractal dimension is a quantitative value from the scale relation on the meaning of fractal, and also a measuring on self-similarity of its structure. The fractal measuring is fractal dimension[6-7]. From the viewpoint of measuring, fractal dimension is extended from integer to fraction, breaking the limitof the general to pology set dimension being integer Fractal dimension,fraction mostly, is dimension extension in Euclidean geometry.There are many definitions on fractal dimension, eg.,similar dimension, Hausdoff dimension, inforation dimension, correlation dimension, capability imension, box-counting dimension etc. , where,Hausdoff dimension is oldest and also most important, for any sets, it is defined as[3].Where, M￡(F) denotes how many unit ￡needed to cover subset F.In thispaper, the Box-Counting dimension (DB) of ,F, is obtained by partitioning the plane with squares grids of side ￡, and the numberof squares that intersect the plane (N(￡)) and is defined as[8].The speech waveform of Chinese command word“Forward”and fractal dimension waveform after Endpoint detection is shown in Fig. 4. 3. 3Improved feature extractions methodConsidering the respective advantages on expressing speech signal of LPCC and fractal dimension,we mix both to be the feature signal, that is, fractal dimension denotes the self2similarity, periodicity and randomness of speech time wave shape, meanwhile LPCC feature is good for speech quality and high on identification rate.Due to ANN′s nonlinearity, self-adaptability, robust and self-learning such obvious advantages, its good classification and input2output reflection ability are suitable to resolve speech recognition problem.Due to the number of ANN input nodes being fixed, therefore time regularization is carried out to the feature parameter before inputted to the neural network[9]. In our experiments, LPCC and fractal dimension of eachsample are need to get through the network of time regularization separately, LPCC is 4-frame data(LPCC1,LPCC2,LPCC3,LPCC4, each frame parameter is 14-D), fractal dimension is regularized to be12-frame data(FD1,FD2,…,FD12, each frame parameter is 1-D), so that the feature vector of each sample has 4*14+1*12=68-D, the order is, the first 56 dimensions are LPCC, the rest 12 dimensions are fractal dimensions. Thus, such mixed feature parameter can show speech linear and nonlinear characteristics as well.Architectures and Features of ASR ASR is a cutting edge technology that allows a computer or even a hand-held PDA (Myers, 2000) to identify words that are read aloud or spoken into any sound-recording device. The ultimate purpose of ASR technology is to allow 100% accuracy with all words that are intelligibly spoken by any person regardless of vocabulary size, background noise, or speaker variables (CSLU, 2002). However, most ASR engineers admit that the current accuracy level for a large vocabulary unit of speech (e.g., the sentence) remains less than 90%. Dragon's Naturally Speaking or IBM's ViaV oice, for example, show a baseline recognition accuracy of only 60% to 80%, depending upon accent, background noise, type of utterance, etc. (Ehsani & Knodt, 1998). More expensive systems that are reported to outperform these two are Subarashii (Bernstein, et al., 1999), EduSpeak (Franco, et al., 2001), Phonepass (Hinks, 2001), ISLE Project (Menzel, et al., 2001) and RAD (CSLU, 2003). ASR accuracy is expected to improve. Among several types of speech recognizers used in ASR products, both implemented and proposed, the Hidden Markov Model (HMM) is one of the most dominant algorithms and has proven to be an effective method of dealing with large units of speech (Ehsani & Knodt, 1998). Detailed descriptions of how the HHM model works go beyond the scope of this paper and can be found in any text concerned with language processing; among the best are Jurafsky & Martin (2000) and Hosom, Cole, and Fanty(2003). Put simply, HMM computes the probable match between the input it receives and phonemes contained in a database of hundreds of native speaker recordings (Hinks, 2003, p. 5). That is, a speech recognizer based on HMM computes how close the phonemes of a spoken input are to a corresponding model, based on probability theory. High likelihood represents good pronunciation; low likelihood represents poor pronunciation (Larocca, et al., 1991).While ASR has been commonly used for such purposes as business dictation and special needs accessibility, its market presence for language learning has increased dramatically in recent years (Aist, 1999; Eskenazi, 1999; Hinks, 2003). Early ASR-based software programs adopted template-based recognition systems which perform pattern matching using dynamic programming or other time normalization techniques (Dalby & Kewley-Port, 1999). These programs include Talk to Me (Auralog, 1995), the Tell Me More Series (Auralog, 2000), Triple-Play Plus (Mackey & Choi, 1998), New Dynamic English (DynEd, 1997), English Discoveries (Edusoft, 1998), and See it, Hear It, SAY IT! (CPI, 1997). Most of these programs do not provide any feedback on pronunciation accuracy beyond simply indicating which written dialogue choice the user has made, based on the closest pattern match. Learners are not told the accuracy of their pronunciation. In particular, Neri, et al. (2002) criticizes the graphical wave forms presented in products such as Talk to Me and Tell Me More becausethey look flashy to buyers, but do not give meaningful feedback to users. The 2000 version of Talk to Me has incorporated more of the features that Hinks (2003), for example, believes are useful to learners:★A visual signal allows learners to compare their intonation to that of the model speaker.★The learners' pronunciation accuracy is scored on a scale of seven (the higher the better).Words whose pronunciation fails to be recognized are highlighted。