请输入或粘贴题目内容
搜题
拍照、语音搜题，请扫码下载APP
题目内容
（请给出正确答案）
A.persuade
B.persuades
C.persuading
D.persuaded
答案
更多“In the justification part, you _____ your audience that your proposal should be enacte”相关的问题
第1题
A justification tries to present a reason to believe its conclusion.()
第2题
第3题
After considering carefully, my plan has gradually come to ________.
A maturity
B sensibility
C justification
D awareness
第4题
The second paragraph mainly tells us that ______.
A．the author sums up the previous research experience
B．the author offers a theoretical background and justification
C．the author tries to make absolute judgments
D．the author points out various approaches
第5题
The propositional content of a sentence gets a contextual meaning when it is judged in a given situation or context. This might be a reason, justification, assumption, explanation, or other functions that the sentence might assume from the context. The functional value of a sentence is derived from the writer's intention in using it, and it is identified from the relationship between this sentence and others in the same text. For example, when it stands alone, the proposition I like the thinking process that goes with it just gives the view of the speaker. But when it follows the sentence I'd take several courses in philosophy (Passage B: Line 23), it assumes the function of giving a reason or justification. The writer uses it to explain why he would take philosophy courses. Sometimes, the writer uses signal words to indicate the functional value or contextual meaning. For instance, but, however" or nevertheless are often used to signal a change of thought, and because, since, or therefore to signal an explanation or reasoning.
第6题
Speech Recognition
语音识别系统
Automatic recognition of speech by machine[1]has been a goal of research for more than four decades and has inspired such science fiction wonders as the computer HAL in Stanley Kubrick's famous movie 2001—A Space Odyssey[2]and the robot R2D2 in the George Lucas classic Star Wars[3]series of movies. However, in spite of the glamour of designing an intelligent machine that can recognize the spoken word and comprehend its meaning, and in spite of the enormous research efforts spent in trying to create such a machine, we are far from[4]achieving the desired goal of a machine that can understand spoken discourse on any subject by all speakers in all environments. Thus, an important question is, What do we mean by "speech recognition by machine". Another important question is, How can we build a series of bridges that will enable us to advance both our knowledge as well as the capabilities of modern speech-recognition systems so that the "holy grail"[5]of conversational speech recognition and understanding by machine is attained？
Because we do not know how to solve the ultimate challenge of speech recognition, our goal here is to give a series of presentations on the fundamental principles of most modern, successful speech-recognition systems so as to provide a framework from which researchers can expand the frontier. We will attempt to avoid making absolute judgments on the relative merits of various approaches to particular speech-recognition problems. Instead we will provide the theoretical background and justification for each topic discussed so that the reader is able to understand why the techniques have proved valuable and how they can be used to benefit practical situations.
One of the most difficult aspects of performing research in speech recognition by machine is its interdisciplinary nature[6], and the tendency of most researchers to apply a monolithic approach to individual problems. Consider the disciplines that have been applied to one or more speech-recognition problems.
1. signal processing—the process of extracting relevant information from the speech signal in an efficient, robust manner. Included in signal processing is the form of spectral analysis used to characterize the time-varying properties of the speech signal as well as various types of signal preprocessing (and postprocessing) to make the speech signal robust to the recording environment (signal enhancement).
2. physics (acoustics)—the science of understanding the relationship between the physical speech signal and the physiological mechanisms (the human vocal tract mechanism)[7]that produced the speech and with which the speech is perceived (the human hearing mechanism).
3. pattern recognition—the set of algorithms used to cluster data to create one or more prototypical patterns of a data ensemble, and to match a pair of patterns on the basis of feature measurements of the patterns.
4. communication and information theory—the procedures for estimating parameters of statistical models; the methods for detecting the presence of particular speech patterns, the set of modern coding and decoding algorithms used to search a large but finite grid for a best path corresponding to a "best" recognized sequence of words.
5. linguistics—the relationships between sounds, words in a language, meaning of spoken words and sense derived from meaning. Included within this discipline are the methodology of grammar and language parsing.
6. physiology—understanding of the higher-order mechanisms within the human central nervous system that account for speech production and perception in human beings. Many modern techniques try to embed this type of knowledge within the framework of artificial neural networks (which depend heavily on several of the above disciplines).
7. computer science—the study of efficient algorithms for implementing, in software or hardware, the various methods used in a practical speech-recognition system.
8. psychology—the science of understanding the factors that enable technology to be used by human beings in practical tasks.
Successful speech-recognition systems require knowledge and expertise from a wide range of disciplines, a range far larger than any single person can possess[8]. Therefore, it is especially important for a researcher to have a good understanding of the fundamentals of speech recognition (so that a range of techniques can be applied to a variety of problems), without necessarily having to be an expert in each aspect of the problem. The purpose is to provide this expertise by giving in-depth discussions of a number of fundamental topics in speech-recognition research.
A general model for speech recognition begins with a user creating a speech signal (speaking) to accomplish a given task. The spoken output is first recognized in the speech signal that is decoded into a series of words that are meaningful according to the syntax, semantics and pragmatics[9]of the recognition task. A higher-level processor that uses a dynamic knowledge representation to modify the syntax, semantics, and pragmatics according to the context of what it has previously recognized obtains the meaning of the recognized words. In this manner, things such as non-sequitors are omitted from consideration at the risk of misunderstanding, but at the gain of minimizing errors for sequentially meaningful inputs. The feedback from the higher-level processing box reduces the complexity of the recognition model by limiting the search for valid input sentences (speech) from the user. The recognition system responds to the user in the form of a voice output, or equivalently, in the form of the requested action being performed, with the user being prompted for more input.
A Brief History of Speech-Recognition Research
Research in automatic speech recognition by machine has been done for almost four decades. To gain an appreciation for the amount of progress achieved over this period, it is worthwhile to briefly review some research highlights[10]. The reader is cautioned that such a review is cursory, at best, and must therefore suffer from errors of judgment as well as omission.
The earliest attempts to devise systems for automatic speech recognition by machine were made in 1950s, at Bell Laboratories. Davis, Biddulph, and Balashek built a system for isolated digit recognition for a single speaker. The system relied heavily on measuring spectral resonances during the vowel region of each digit. Another effort of note in this period was the vowel recognizer of Forgie and Forgie, constructed at MIT Lincoln Laboratories[11]in 1959, in which 10 vowels embedded in a /b/-vowel-/t/ format were recognized in a speaker-independent manner. Again, a filter bank analyzer was used to provide special information and a time-varying estimate of the vocal tract resonances was made to decide which vowel was spoken.
In the 1960s several fundamental ideas in speech recognition surfaced and were published. However, the decade started with several Japanese laboratories entering the recognition arena and building special-purpose hardware as part of their systems. In the 1960s three key research projects were initiated that have had major implications on the research and development of speech recognition for the past 20 years. The first of these projects was from the effort of Martin and his colleagues at RCA[12]Laboratories, beginning in the late 1960s, to develop realistic solutions to the problems associated with nonuniformity of time scales in speech events. At about the same time, in the Soviet Union, Vintsyuk proposed the use of dynamic programming methods for time aligning a pair of speech utterances. Although the essence of the concepts of dynamic time warping, as well as rudimentary versions of the algorithms for connected word recognition, were embodied in Vintsyuk's work, it was largely unknown in the West and did not come to light until the early 1980s; this was long after the more formal methods were proposed and implemented by others.
A final achievement of note in the 1960s was the pioneering research of Reddy in the field of continuous speech recognition by dynamic tracking of phonemes. Reddy's research eventually spawned a long and highly successful speech-recognition research program at Carnegie Mellon University, which, to this day, remains a world leader in continuous- speech-recognition systems.
In the 1970s speech-recognition research achieved a number of significant milestones. First was the area of isolated word or discrete utterance recognition. The Japanese research showed how dynamic programming methods could be successfully applied; and the American research showed how the ideas of linear predictive coding (LPC)[13], which had already been successfully used in low-bit-rate speech coding, could be extended to speech- recognition systems through the use of an appropriate distance measure based on LPC spectral parameters.
Another milestone of the 1970s was the beginning of a longstanding, highly successful group effort in large vocabulary speech recognition at IBM, in which researchers studied three distinct simple database queries, the laser patent text language for transcribing laser patents, and the office correspondence task, called Tangora, for dictation of simple memos.
Speech research in the 1980s was characterized by a shift in technology from template- based approaches to statistical modeling methods—especially the hidden Markov model approach. Although the methodology of hidden Markov modeling (HMM)[14]was well known and understood in a few laboratories, it was not until widespread publication of the methods and theory of HMMs, in the mid-1980s, that the technique became widely applied in virtually every speech-technology that was recognition research laboratory in the world.
Another "new" technology that was reintroduced in the late 1980s was the idea of applying neural networks to problems in speech recognition. Neural networks were first introduced in the 1950s, but they did not prove useful initially because they had many practical problems. In the 1980s, however, a deeper understanding of the strengths and limitations of the technology was obtained, as well as the relationships of the technology to classical signal classification methods. Several new ways of implementing systems were also proposed.
Finally, the 1980s was a decade in which a major impetus was given to large vocabulary, continuous-speech-recognition systems by the Defense Advanced Research Projects Agency (DARPA) community[15], which sponsored a large research program aimed at achieving high word accuracy for a 1,000-word, continuous-speech-recognition, database management task. The DARPA program has continued into the 1990s, with emphasis shifting to natural language front ends to the recognizer, and the task shifting to retrieval of air travel information. At the same time, speech-recognition technology has been increasingly used within telephone networks to automate as well as enhance operator services.
Notes
[1] automatic recognition of speech by machine: 计算机自动语言识别。
[2] science fiction wonders as the computer HAL in Stanley Kubrick's famous movie 2001—A Space Odyssey: 斯坦利·库布里克著名的科幻电影《 2001年太空漫游 》(1968),也有人译成《 2001年太空奥德赛 》。荷马史诗《奥德赛 》(Odyssey)中的主人公奥德修斯(Odysseus)从特洛伊战争回来的途中花了20年时间才最终到家。他经历了不可思议的冒险，甚至还到阴间同死者对话。因此，Odyssey指任何追求某个目标漫长、复杂的旅途，也可指一个精神上或心理上长期追求、探索的历程。
[3] in the George Lucas classic Star Wars：乔治·卢卡斯的经典科幻电影《星球大战》(1977)。Star Wars，美国战略防御系统的别称。此系统规划于20世纪80年代，得到里根总统支持。系统采用可在太空运行的激光武器来击落敌方导弹。此别称来源于“星球大战”的科幻片，该片反映了这个系统的高科技特点。
[4] far from: 远不是，远非，如：His work is far from good.(他的工作很不好。)
[5] holy grail: 耶稣在最后的晚餐时用的杯(或盘)；长期以来梦寐以求的东西。
[6] its interdisciplinary nature：它的跨学科特性。
[7] the physiological mechanisms (the human vocal tract mechanism)：生理机制(人的声道发声机制)。
[8] a range far larger than any single person can possess: far larger是形容词比较级短语，作定语修饰名词range，此句是意思是：比任何一个人的知识面都要广得多。
[9] the syntax，semantics and pragmatics：句法、语义学和语用学。
[10] is worthwhile to briefly review some research highlights. 简单回顾研究上的重大进展是有益的。
[11] MIT Lincoln Laboratories:(美国)麻省理工学院林肯实验室(Massachusetts Institute of Technology)。
[12] RAC: 美国无线电公司(Radio Corporation of America)。
[13] linear predictive coding(LPC)：线性预测编码法，一种语音压缩技术，它将语音产生的机理模型化为一个离散的、时变的、线性递归滤波器，该滤波器又进一步模型化为一个正反馈回路中的自适应线性预测器。
[14] Hidden Markov modeling HMM: 隐式马尔可夫模型。
[15] Defense Advanced Research Projects Agency (DARPA) community: (美国)国防部高级研究系统计划署。
Choose the best answer:
