语音信号处理毕业论文中英文资料外文翻译文献
毕业文献翻译
语音信号处理毕业论文中英文资料外文翻译文献
语音识别
在计算机技术中，语音识别是指为了达到说话者发音而由计算机生成的功能，利用计算机识别人类语音的技术。（例如，抄录讲话的文本，数据项;经营电子和机械设备;电话的自动化处理），是通过所谓的自然语言处理的计算机语音技术的一个重要元素。通过计算机语音处理技术，来自语音发音系统的由人类创造的声音，包括肺，声带和舌头，通过接触，语音模式的变化在婴儿期、儿童学习认识有不同的模式，尽管由不同人的发音，例如，在音调，语气，强调，语调模式不同的发音相同的词或短语，大脑的认知能力，可以使人类实现这一非凡的能力。在撰写本文时（2008年），我们可以重现，语音识别技术不只表现在有限程度的电脑能力上，在其他许多方面也是有用的。
语音识别技术的挑战
古老的书写系统,要回溯到苏美尔人的六千年前。他们可以将模拟录音通过留声机进行语音播放，直到1877年。然而，由于与语音识别各种各样的问题，语音识别不得不等待着计算机的发展。
首先,演讲不是简单的口语文本——同样的道理,戴维斯很难捕捉到一个note-for-note曲作为乐谱。人类所理解的词、短语或句子离散与清晰的边界实际上是将信号连续的流,而不是听起来: I went to the store yesterday昨天我去商店。单词也可以混合,用Whadd ayawa吗?这代表着你想要做什么。第二,没有一对一的声音和字母之间的相关性。在英语,有略多于5个元音字母——a,e,i,o,u,有时y和w。有超过二十多个不同的元音, 虽然,精确统计可以取决于演讲者的口音而定。但相反的问题也会发生,在那里一个以上的信号能再现某一特定的声音。字母C可以有相同的字母K的声音，如蛋糕，或作为字母S，如柑橘。
此外,说同一语言的人使用不相同的声音,即语言不同,他们的声音语音或模式的组织，有不同的口音。例如“水”这个词,wadder可以显著watter，woader wattah等等。每个
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人都有独特的音量——男人说话的时候,一般开的最低音，妇女和儿童具有更高的音高(虽然每个人都有广泛的变异和重叠)。发音可以被邻近的声音、说话者的速度和说话者的健康状况所影响，当一个人感冒的时候，就要考虑发音的变化。
最后,考虑到不是所有的语音都是有意义的声音组成。通常语音自身是没有任何意义的，但有些用作分手话语以传达说话人的微妙感情或动机的信息:哦,就像,你知道,好的。也有一些听起来都不认为是字，这是一项词性的：呃,嗯,嗯。嗽、打喷嚏、谈笑风生、呜咽,甚至打嗝的可以成为上述的内容之一。在噪杂的地方与环境自身的噪声中，即使语音识别也是困难的。
“我昨天去了商店”的波形图
“我昨天去了商店”的光谱图
语音识别的发展史
尽管困难重重，语音识别技术却随着数字计算机的诞生一直被努力着。早在1952年，研究人员在贝尔实验室就已开发出了一种自动数字识别器，取名“奥黛丽”。如果说话的人是男性，并且发音者在词与词之间停顿350毫秒并把把词汇限制在1—9之间的数字，再加上“哦”，另外如果这台机器能够调整到适应说话者的语音习惯，奥黛丽的精确度将达到97℅—99℅，如果识别器不能够调整自己，那么精确度将低至60℅.
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奥黛丽通过识别音素或者两个截然不同的声音工作。这些因素与识别器经训练产生的参考音素是有关联的。在接下来的20年里研究人员花了大量的时间和金钱来改善这个概念，但是少有成功。计算机硬件突飞猛进、语音合成技术稳步提高，乔姆斯基的生成语法理论认为语言可以被程序性地分析。然而，这些似乎并没有提高语音识别技术。乔姆斯基和哈里的语法生成工作也导致主流语言学放弃音素概念，转而选择将语言的声音模式分解成更小、更易离散的特征。
1969年皮尔斯坦率地写了一封信给美国声学学会的会刊，大部分关于语音识别的研究成果都发表在上面。皮尔斯是卫星通信的先驱之一，并且是贝尔实验室的执行副主任，贝尔实验室在语音识别研究中处于领先地位。皮尔斯说所有参与研究的人都是在浪费时间和金钱。
如果你认为一个人之所以从事语音识别方面的研究是因为他能得到金钱，那就太草率了。这种吸引力也许类似于把水变成汽油、从海水中提取黄金、治愈癌症或者登月的诱惑。一个人不可能用削减肥皂成本10℅的方法简单地得到钱。如果想骗到人，他要用欺诈和诱惑。
皮尔斯1969年的信标志着在贝尔实验室持续了十年的研究结束了。然而，国防研究机构ARPA选择了坚持下去。1971年他们资助了一项开发一种语音识别器的研究计划，这种语音识别器要能够处理至少1000个词并且能够理解相互连接的语音，即在语音中没有词语之间的明显停顿。这种语音识别器能够假设一种存在轻微噪音背景的环境，并且它不需要在真正的时间中工作。
到1976年，三个承包公司已经开发出六种系统。最成功的是由卡耐基麦隆大学开发的叫做“Harpy”的系统。“Harpy”比较慢，四秒钟的句子要花费五分多钟的时间来处理。并且它还要求发音者通过说句子来建立一种参考模型。然而，它确实识别出了1000个词汇，并且支持连音的识别。
研究通过各种途径继续着，但是“Harpy”已经成为未来成功的模型。它应用隐马尔科夫模型和统计模型来提取语音的意义。本质上，语音被分解成了相互重叠的声音片段和被认为最可能的词或词的部分所组成的几率模型。整个程序计算复杂，但它是最成功的。
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在1970s到1980s之间，关于语音识别的研究继续进行着。到1980s，大部分研究者都在使用隐马尔科夫模型，这种模型支持着现代所有的语音识别器。在1980s后期和1990s，DARPA资助了一些研究。第一项研究类似于以前遇到的挑战，即1000个词汇量，但是这次要求更加精确。这个项目使系统词汇出错率从10℅下降了一些。其余的研究项目都把精力集中在改进算法和提高计算效率上。
2001年微软发布了一个能够与0ffice XP 同时工作的语音识别系统。它把50年来这项技术的发展和缺点都包含在内了。这个系统必须用大作家的作品来训练为适应某种指定的声音，比如埃德加爱伦坡的厄舍古屋的倒塌和比尔盖茨的前进的道路。即使在训练之后，该系统仍然是脆弱的，以至于还提供了一个警告：“如果你改变使用微软语音识别系统的地点导致准确率将降低，请重新启动麦克风”。从另一方面来说，该系统确实能够在真实的时间中工作，并且它确实能识别连音。
语音识别的今天
技术
当今的语音识别技术着力于通过共振和光谱分析来对我们的声音产生的声波进行数学分析。计算机系统第一次通过数字模拟转换器记录了经过麦克风传来的声波。那种当我们说一个词的时候所产生的模拟的或者持续的声波被分割成了一些时间碎片，然后这些碎片按照它们的振幅水平被度量，振幅是指从一个说话者口中产生的空气压力。为了测量振幅水平并且将声波转换成为数字格式，现在的语音识别研究普遍采用了奈奎斯特—香农定理。
奈奎斯特—香农定理
奈奎斯特—香农定理是在1928年研究发现的，该定理表明一个给定的模拟频率能够由一个是原始模拟频率两倍的数字频率重建出来。奈奎斯特证明了该规律的真实性，因为一个声波频率必须由于压缩和疏散各取样一次。例如，一个20kHz的音频信号能准确地被表示为一个44.1kHz的数字信号样本。
工作原理
语音识别系统通常使用统计模型来解释方言，口音，背景噪音和发音的不同。这些模型已经发展到这种程度，在一个安静的环境中准确率可以达到90℅以上。然而每一个
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公司都有它们自己关于输入处理的专项技术，存在着4种关于语音如何被识别的共同主题。
1.基于模板：这种模型应用了内置于程序中的语言数据库。当把语音输入到系统中后，识别器利用其与数据库的匹配进行工作。为了做到这一点，该程序使用了动态规划算法。这种语音识别技术的衰落是因为这个识别模型不足以完成对不在数据库中的语音类型的理解。
2.基于知识：基于知识的语音识别技术分析语音的声谱图以收集数据和制定规则，这些数据和规则回馈与操作者的命令和语句等值的信息。这种识别技术不适用关于语音的语言和语音知识。
3.随机：随机语音识别技术在今天最为常见。随机语音分析方法利用随机概率模型来模拟语音输入的不确定性。最流行的随机概率模型是HMM（隐马尔科夫模型）。如下所示：
Yt是观察到的声学数据，p（W）是一个特定词串的先天随机概率，p（Yt∣W）是在给定的声学模型中被观察到的声学数据的概率，W是假设的词汇串。在分析语音输入的时候，HMM被证明是成功的，因为该算法考虑到了语言模型，人类说话的声音模型和已知的所有词汇。
1.联结：在联结主义语音识别技术当中，关于语音输入的知识是这样获得的，即分析输入的信号并从简单的多层感知器中用多种方式将其储存在延时神经网络中。
如前所述，利用随机模型来分析语言的程序是今天最流行的，并且证明是最成功的。识别指令
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当今语音识别软件最重要的目标是识别指令。这增强了语音软件的功能。例如微软Sync 被装进了许多新型汽车里面，据说这可以让使用者进入汽车的所有电子配件和免提。这个软件是成功的。它询问使用者一系列问题并利用常用词汇的发音来得出语音恒量。这些常量变成了语音识别技术算法中的一环，这样以后就能够提供更好的语音识别。当今的技术评论家认为这项技术自20世纪90年代开始已经有了很大进步，但是在短时间内不会取代手控装置。
听写
关于指令识别的第二点是听写。就像接下来讨论的那样，今天的市场看重听写软件在转述医疗记录、学生试卷和作为一种更实用的将思想转化成文字方面的价值。另外，许多公司看重听写在翻译过程中的价值，在这个过程中，使用者可以把他们的语言翻译成为信件，这样使用者就可以说给他们母语中另一部分人听。在今天的市场上，关于该软件的生产制造已经存在。
语句翻译中存在的错误
当语音识别技术处理你的语句的时候，它们的准确率取决于它们减少错误的能力。它们在这一点上的评价标准被称为单个词汇错误率（SWER）和指令成功率（CSR）。当一个句子中一个单词被弄错，那就叫做单个词汇出错。因为SWERs在指令识别系统中存在，它们在听写软件中最为常见。指令成功率是由对指令的精确翻译决定的。一个指令陈述可能不会被完全准确的翻译，但识别系统能够利用数学模型来推断使用者想要发出的指令。
商业
主要的语音技术公司
随着语音技术产业的发展，更多的公司带着他们新的产品和理念进入这一领域。下面是一些语音识别技术领域领军公司名单（并非全部）NICE Systems（NASDAQ:NICE and Tel Aviv：Nice），该公司成立于1986年，总部设在以色列，它专长于数字记录和归档技术。他们在2007年收入5.23亿美元。欲了解更多信息，请访问https://www.360docs.net/doc/cd15276757.html,
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Verint系统公司（OTC：VRNT），总部设在纽约的梅尔维尔，创立于1994年把自己定位为“劳动力优化智能解决方案，IP视频，通讯截取和公共安全设备的领先供应商。详细信息，请访问https://www.360docs.net/doc/cd15276757.html,
Nuance公司（纳斯达克股票代码：NUAN）总部设在伯灵顿，开发商业和客户服务使用语音和图像技术。欲了解更多信息，请访问https://www.360docs.net/doc/cd15276757.html, Vlingo，总部设在剑桥，开发与无线/移动技术对接的语音识别技术。Vlingo最近与雅虎联手合作，为雅虎的移动搜索服务—一键通功能提供语音识别技术。欲了解更多信息，请访问https://www.360docs.net/doc/cd15276757.html,
在语音技术领域的其他主要公司包括：Unisys，ChaCha，SpeechCycle，Sensory，微软的Tellme公司，克劳斯纳技术等等。
专利侵权诉讼
考虑到这两项业务和技术的高度竞争性，各公司之间有过无数次的专利侵权诉讼并不奇怪。在开发语音识别设备所涉及的每个元素都可以作为一个单独的技术申请专利。使用已经被另一家公司或个人申请专利的技术，即使这项技术是你自己独立研发的，你也可能被要求赔偿，并并可能不公正地禁止你以后使用该项技术。语音产业中的政治和商业紧紧地与语音技术的发展联系在一起，因此，必须认识到可能阻碍该行业的进一步发展的政治和法律障碍。下面是对一些专利侵权诉讼的叙述。应当指出，目前有许多这样的诉讼立案，许多诉讼案被推上法庭。
语音识别未来的发展
今后的发展趋势和应用
医疗行业
医疗行业有多年来一直在宣传电子病历(EMR)。不幸的是,产业迟迟不能够满足EMRs,一些公司断定原因是由于数据的输入。没有足够的人员将大量的病人信息输入成为电子格式,因此,纸质记录依然盛行。一家叫Nuance(也出现在其他领域,软件开发者称为龙指令)相信他们可以找到一市场将他们的语音识别软件出售那些更喜欢声音而非手写输入病人信息的医生。
军事
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国防工业研究语音识别软件试图将其应用复杂化而非更有效率和亲切。为了使驾驶员更快速、方便地进入需要的数据库，语音识别技术是目前正在飞机驾驶员座位下面的显示器上进行试验。
军方指挥中心同样正在尝试利用语音识别技术在危急关头用快速和简易的方式进入他们掌握的大量资料库。另外，军方也为了照顾病员涉足EMR。军方宣布，正在努力利用语音识别软件把数据转换成为病人的记录。
摘自：https://www.360docs.net/doc/cd15276757.html,/wiki/Speech_Recognition
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附：英文原文
Speech Recognition
In computer technology, Speech Recognition refers to the recognition of human speech by computers for the performance of speaker-initiated computer-generated functions (e.g., transcribing speech to text; data entry; operating electronic and mechanical devices; automated processing of telephone calls) —a main element of so-called natural language processing through computer speech technology. Speech derives from sounds created by the human articulatory system, including the lungs, vocal cords, and tongue. Through exposure to variations in speech patterns during infancy, a child learns to recognize the same words or phrases despite different modes of pronunciation by different people—e.g., pronunciation differing in pitch, tone, emphasis, intonation pattern. The cognitive ability of the brain enables humans to achieve that remarkable capability. As of this writing (2008), we can reproduce that capability in computers only to a limited degree, but in many ways still useful.
The Challenge of Speech Recognition
Writing systems are ancient, going back as far as the Sumerians of 6,000 years ago. The phonograph, which allowed the analog recording and playback of speech, dates to 1877. Speech recognition had to await the development of computer, however, due to multifarious problems with the recognition of speech.
First, speech is not simply spoken text--in the same way that Miles Davis playing So What can hardly be captured by a note-for-note rendition as sheet music. What humans understand as discrete words, phrases or sentences with clear boundaries are actually delivered as a continuous stream of sounds: Iwenttothestoreyesterday, rather than I went to the store yesterday. Words can also blend, with Whaddayawa? representing What do you want? Second, there is no one-to-one correlation between the sounds and letters. In English, there are slightly more than five vowel letters--a, e, i, o, u, and sometimes y and w. There are more than twenty different vowel sounds, though, and the exact count can vary depending on the accent of the speaker. The reverse problem also occurs, where more than one letter can
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represent a given sound. The letter c can have the same sound as the letter k, as in cake, or as the letter s, as in citrus.
In addition, people who speak the same language do not use the same sounds, i.e. languages vary in their phonology, or patterns of sound organization. There are different accents--the word 'water' could be pronounced watter, wadder, woader, wattah, and so on. Each person has a distinctive pitch when they speak--men typically having the lowest pitch, women and children have a higher pitch (though there is wide variation and overlap within each group.) Pronunciation is also colored by adjacent sounds, the speed at which the user is talking, and even by the user's health. Consider how pronunciation changes when a person has a cold. Lastly, consider that not all sounds consist of meaningful speech. Regular speech is filled with interjections that do not have meaning in themselves, but serve to break up discourse and convey subtle information about the speaker's feelings or intentions: Oh, like, you know, well. There are also sounds that are a part of speech that are not considered words: er, um, uh. Coughing, sneezing, laughing, sobbing, and even hiccupping can be a part of what is spoken. And the environment adds its own noises; speech recognition is difficult even for humans in noisy places.
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History of Speech Recognition
Despite the manifold difficulties, speech recognition has been attempted for almost as long as there have been digital computers. As early as 1952, researchers at Bell Labs had developed an Automatic Digit Recognizer, or "Audrey". Audrey attained an accuracy of 97 to 99 percent if the speaker was male, and if the speaker paused 350 milliseconds between words, and if the speaker limited his vocabulary to the digits from one to nine, plus "oh", and if the machine could be adjusted to the speaker's speech profile. Results dipped as low as 60 percent if the recognizer was not adjusted.
Audrey worked by recognizing phonemes, or individual sounds that were considered distinct from each other. The phonemes were correlated to reference models of phonemes that were generated by training the recognizer. Over the next two decades, researchers spent large amounts of time and money trying to improve upon this concept, with little success. Computer hardware improved by leaps and bounds, speech synthesis improved steadily, and Noam Chomsky's idea of generative grammar suggested that language could be analyzed programmatically. None of this, however, seemed to improve the state of the art in speech recognition. Chomsky and Halle's generative work in phonology also led mainstream linguistics to abandon the concept of the "phoneme" altogether, in favour of breaking down the sound patterns of language into smaller, more discrete "features".
In 1969, John R. Pierce wrote a forthright letter to the Journal of the Acoustical Society of America, where much of the research on speech recognition was published. Pierce was one of the pioneers in satellite communications, and an executive vice president at Bell Labs, which was a leader in speech recognition research. Pierce said everyone involved was wasting time and money.
It would be too simple to say that work in speech recognition is carried out simply because one can get money for it. . . .The attraction is perhaps similar to the attraction of schemes for turning water into gasoline, extracting gold from the sea, curing cancer, or going to the moon. One doesn't attract thoughtlessly given dollars by means of schemes for cutting the cost of soap by 10%. To sell suckers, one uses deceit and offers glamor.
毕业文献翻译
Pierce's 1969 letter marked the end of official research at Bell Labs for nearly a decade. The defense research agency ARPA, however, chose to persevere. In 1971 they sponsored a research initiative to develop a speech recognizer that could handle at least 1,000 words and understand connected speech, i.e., speech without clear pauses between each word. The recognizer could assume a low-background-noise environment, and it did not need to work in real time.
By 1976, three contractors had developed six systems. The most successful system, developed by Carnegie Mellon University, was called Harpy. Harpy was slow—a four-second sentence would have taken more than five minutes to process. It also still required speakers to 'train' it by speaking sentences to build up a reference model. Nonetheless, it did recognize a thousand-word vocabulary, and it did support connected speech.
Research continued on several paths, but Harpy was the model for future success. It used hidden Markov models and statistical modeling to extract meaning from speech. In essence, speech was broken up into overlapping small chunks of sound, and probabilistic models inferred the most likely words or parts of words in each chunk, and then the same model was applied again to the aggregate of the overlapping chunks. The procedure is computationally intensive, but it has proven to be the most successful.
Throughout the 1970s and 1980s research continued. By the 1980s, most researchers were using hidden Markov models, which are behind all contemporary speech recognizers. In the latter part of the 1980s and in the 1990s, DARPA (the renamed ARPA) funded several initiatives. The first initiative was similar to the previous challenge: the requirement was still a one-thousand word vocabulary, but this time a rigorous performance standard was devised. This initiative produced systems that lowered the word error rate from ten percent to a few percent. Additional initiatives have focused on improving algorithms and improving computational efficiency.
In 2001, Microsoft released a speech recognition system that worked with Office XP. It neatly encapsulated how far the technology had come in fifty years, and what the limitations still were. The system had to be trained to a specific user's voice, using the works of great authors
毕业文献翻译
that were provided, such as Edgar Allen Poe's Fall of the House of Usher, and Bill Gates' The Way Forward. Even after training, the system was fragile enough that a warning was provided, "If you change the room in which you use Microsoft Speech Recognition and your accuracy drops, run the Microphone Wizard again." On the plus side, the system did work in real time, and it did recognize connected speech.
Speech Recognition Today
Technology
Current voice recognition technologies work on the ability to mathematically analyze the sound waves formed by our voices through resonance and spectrum analysis. Computer systems first record the sound waves spoken into a microphone through a digital to analog converter. The analog or continuous sound wave that we produce when we say a word is sliced up into small time fragments. These fragments are then measured based on their amplitude levels, the level of compression of air released from a person’s mouth. To measure the amplitudes and convert a sound wave to digital format the industry has commonly used the Nyquist-Shannon Theorem.
Nyquist-Shannon Theorem
The Nyquist –Shannon theorem was developed in 1928 to show that a given analog frequency is most accurately recreated by a digital frequency that is twice the original analog frequency. Nyquist proved this was true because an audible frequency must be sampled once for compression and once for rarefaction. For example, a 20 kHz audio signal can be accurately represented as a digital sample at 44.1 kHz.
How it Works
Commonly speech recognition programs use statistical models to account for variations in dialect, accent, background noise, and pronunciation. These models have progressed to such an extent that in a quiet environment accuracy of over 90% can be achieved. While every company has their own proprietary technology for the way a spoken input is processed there exists 4 common themes about how speech is recognized.
毕业文献翻译
? 1. Template-Based: This model uses a database of speech patterns built into the program. After receiving voice input into the system recognition occurs by matching
the input to the database. To do this the program uses Dynamic Programming
algorithms. The downfall of this type of speech recognition is the inability for the
recognition model to be flexible enough to understand voice patterns unlike those in
the database.
? 2. Knowledge-Based: Knowledge-based speech recognition analyzes the spectrograms of the speech to gather data and create rules that return values equaling what
commands or words the user said. Knowledge-Based recognition does not make use of linguistic or phonetic knowledge about speech.
? 3. Stochastic: Stochastic speech recognition is the most common today. Stochastic methods of voice analysis make use of probability models to model the uncertainty of the spoken input. The most popular probability model is use of HMM (Hidden
Markov Model) is shown below.
Yt is the observed acoustic data, p(W) is the a-priori probability of a particular word string, p(Yt|W) is the probability of the observed acoustic data given the acoustic models, and W is the hypothesised word string. When analyzing the spoken input the HMM has proven to be successful because the algorithm takes into account a language model, an acoustic model of how humans speak, and a lexicon of known words.
? 4. Connectionist: With Connectionist speech recognition knowledge about a spoken input is gained by analyzing the input and storing it in a variety of ways from simple
multi-layer perceptrons to time delay neural nets to recurrent neural nets.
毕业文献翻译
As stated above, programs that utilize stochastic models to analyze spoken language are most common today and have proven to be the most successful.
Recognizing Commands
The most important goal of current speech recognition software is to recognize commands. This increases the functionality of speech software. Software such as Microsost Sync is built into many new vehicles, supposedly allowing users to access all of the car’s electronic accessories, hands-free. This software is adaptive. It asks the user a series of questions and utilizes the pronunciation of commonly used words to derive speech constants. These constants are then factored into the speech recognition algorithms, allowing the application to provide better recognition in the future. Current tech reviewers have said the technology is much improved from the early 1990’s but will not be replacing hand controls any time soon. Dictation
Second to command recognition is dictation. Today's market sees value in dictation software as discussed below in transcription of medical records, or papers for students, and as a more productive way to get one's thoughts down a written word. In addition many companies see value in dictation for the process of translation, in that users could have their words translated for written letters, or translated so the user could then say the word back to another party in their native language. Products of these types already exist in the market today.
Errors in Interpreting the Spoken Word
As speech recognition programs process your spoken words their success rate is based on their ability to minimize errors. The scale on which they can do this is called Single Word Error Rate (SWER) and Command Success Rate (CSR). A Single Word Error is simply put, a misunderstanding of one word in a spoken sentence. While SWERs can be found in Command Recognition Programs, they are most commonly found in dictation software. Command Success Rate is defined by an accurate interpretation of the spoken command. All words in a command statement may not be correctly interpreted, but the recognition program is able to use mathematical models to deduce the command the user wants to execute.
毕业文献翻译
Business
Major Speech Technology Companies
As the speech technology industry grows, more companies emerge into this field bring with them new products and ideas. Some of the leaders in voice recognition technologies (but by no means all of them) are listed below.
NICE Systems (NASDAQ: NICE and Tel Aviv: Nice), headquartered in Israel and founded in 1986, specialize in digital recording and archiving technologies. In 2007 they made $523 million in revenue in 2007. For more information visit https://www.360docs.net/doc/cd15276757.html,.
Verint Systems Inc.(OTC:VRNT), headquartered in Melville, New York and founded in 1994 self-define themselves as “A leading provider of actionable intelligence solutions for work force optimization, IP video, communications interception, and public safety.”[9] For more information visit https://www.360docs.net/doc/cd15276757.html,.
Nuance (NASDAQ: NUAN) headquartered in Burlington, develops speech and image technologies for business and customer service uses. For more information visit https://www.360docs.net/doc/cd15276757.html,/.
Vlingo, headquartered in Cambridge, MA, develops speech recognition technology that interfaces with wireless/mobile technologies. Vlingo has recently teamed up with Yahoo! providing the speech recognition technology for Yahoo!’s mob ile search service, oneSearch. For more information visit https://www.360docs.net/doc/cd15276757.html,
Other major companies involved in Speech Technologies include: Unisys, ChaCha, SpeechCycle, Sensory, Microsoft's Tellme, Klausner Technologies and many more.
Patent Infringement Lawsuits
Given the highly competitive nature of both business and technology, it is not surprising that there have been numerous patent infringement lawsuits brought by various speech companies. Each element involved in developing a speech recognition device can be claimed as a separate technology, and hence patented as such. Use of a technology, even if it is independently
毕业文献翻译
developed, that is patented by another company or individual is liable to monetary compensation and often results in injunctions preventing companies from henceforth using the technology. The politics and business of the speech industry are tightly tied to speech technology development, it is therefore important to recognize legal and political barriers that may impede further developments in this industry. Below are a number of descriptions of patent infringement lawsuits. It should be noted that there are currently many more such suits on the docket, and more being brought to court every day.
The Future of Speech Recognition
Future Trends & Applications
The Medical Industry
For years the medical industry has been touting electronic medical records (EMR). Unfortunately the industry has been slow to adopt EMRs and some companies are betting that the reason is because of dat a entry. There isn’t enough people to enter the multitude of current patient’s data into electronic format and because of that the paper record prevails. A company called Nuance (also featured in other areas here, and developer of the software called Dragon Dictate) is betting that they can find a market selling their voice recognition software to physicians who would rather speak patients' data than handwrite all medical information into a person’s file.
The Military
The Defense industry has researched voice recognition software in an attempt to make complex user intense applications more efficient and friendly. Currently voice recognition is being experimented with cockpit displays in aircraft under the context that the pilot could access needed data faster and easier.
Command Centers are also looking to use voice recognition technology to search and access the vast amounts of database data under their control in a quick and concise manner during
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situations of crisis. In addition the military has also jumped onboard with EMR for patient care. The military has voiced its commitment to utilizing voice recognition software in transmitting data into patients' records.
FROM：https://www.360docs.net/doc/cd15276757.html,/wiki/Speech_Recognition
毕业论文外文翻译模版
吉林化工学院理学院 毕业论文外文翻译English Title（Times New Roman ，三号） 学生学号：08810219 学生姓名：袁庚文 专业班级：信息与计算科学0802 指导教师：赵瑛 职称副教授 起止日期：2012.2.27～2012.3.14 吉林化工学院 Jilin Institute of Chemical Technology
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毕业论文英文参考文献与译文
Inventory management Inventory Control On the so-called "inventory control", many people will interpret it as a "storage management", which is actually a big distortion. The traditional narrow view, mainly for warehouse inventory control of materials for inventory, data processing, storage, distribution, etc., through the implementation of anti-corrosion, temperature and humidity control means, to make the custody of the physical inventory to maintain optimum purposes. This is just a form of inventory control, or can be defined as the physical inventory control. How, then, from a broad perspective to understand inventory control? Inventory control should be related to the company's financial and operational objectives, in particular operating cash flow by optimizing the entire demand and supply chain management processes (DSCM), a reasonable set of ERP control strategy, and supported by appropriate information processing tools, tools to achieved in ensuring the timely delivery of the premise, as far as possible to reduce inventory levels, reducing inventory and obsolescence, the risk of devaluation. In this sense, the physical inventory control to achieve financial goals is just a means to control the entire inventory or just a necessary part; from the perspective of organizational functions, physical inventory control, warehouse management is mainly the responsibility of The broad inventory control is the demand and supply chain management, and the whole company's responsibility. Why until now many people's understanding of inventory control, limited physical inventory control? The following two reasons can not be ignored: First, our enterprises do not attach importance to inventory control. Especially those who benefit relatively good business, as long as there is money on the few people to consider the problem of inventory turnover. Inventory control is simply interpreted as warehouse management, unless the time to spend money, it may have been to see the inventory problem, and see the results are often very simple procurement to buy more, or did not do warehouse departments . Second, ERP misleading. Invoicing software is simple audacity to call it ERP, companies on their so-called ERP can reduce the number of inventory, inventory control, seems to rely on their small software can get. Even as SAP, BAAN ERP world, the field of
概率论毕业论文外文翻译
Statistical hypothesis testing Adriana Albu，Loredana Ungureanu Politehnica University Timisoara,adrianaa@aut.utt.ro Politehnica University Timisoara,loredanau@aut.utt.ro Abstract In this article,we present a Bayesian statistical hypothesis testing inspection, testing theory and the process Mentioned hypothesis testing in the real world and the importance of, and successful test of the Notes. Key words Bayesian hypothesis testing; Bayesian inference;Test of significance Introduction A statistical hypothesis test is a method of making decisions using data, whether from a controlled experiment or an observational study (not controlled). In statistics, a result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level. The phrase "test of significance" was coined by Ronald Fisher: "Critical tests of this kind may be called tests of significance, and when such tests are available we may discover whether a second sample is or is not significantly different from the first."[1] Hypothesis testing is sometimes called confirmatory data analysis, in contrast to exploratory data analysis. In frequency probability,these decisions are almost always made using null-hypothesis tests. These are tests that answer the question Assuming that the null hypothesis is true, what is the probability of observing a value for the test statistic that is at [] least as extreme as the value that was actually observed?) 2 More formally, they represent answers to the question, posed before undertaking an experiment,of what outcomes of the experiment would lead to rejection of the null hypothesis for a pre-specified probability of an incorrect rejection. One use of hypothesis testing is deciding whether experimental results contain enough information to cast doubt on conventional wisdom. Statistical hypothesis testing is a key technique of frequentist statistical inference. The Bayesian approach to hypothesis testing is to base rejection of the hypothesis on the posterior probability.[3][4]Other approaches to reaching a decision based on data are available via decision theory and optimal decisions. The critical region of a hypothesis test is the set of all outcomes which cause the null hypothesis to be rejected in favor of the alternative hypothesis. The critical region is usually denoted by the letter C. One-sample tests are appropriate when a sample is being compared to the population from a hypothesis. The population characteristics are known from theory or are calculated from the population.
毕业论文参考文献格式示例
例： 参考文献： [1]毛蕴诗. 跨国公司战略竞争与国际直接投资[M].广州: 中山大学出版社 [2]ALEXANDER N. International Retailing [M].Oxford:Blackwell Business,1997 .日本税法[M].战宪斌,郑林根,译.北京:法律出版社.信息技术与信息服务[M]//许厚泽,赵其国.信息技术与应用.,於方,蒋红强,等. 建立中国绿色GDP 核算体系:机遇、挑战与对策[C]//潘岳,绿色GDP 核算体系国际研讨会论文集. 北京:中国环境科学出版社, 2004:35-42. 黄祖洽.软凝聚态物理研究进展[J].北京师范大学学报:自然科学版,2005,41(1) :N, MYERS H. European Retail Expansion in South East Asia[J].European 1999,34(2): 45-50. 丁文祥.数字革命与竞争国际化[N]. 中国青年报, 2000-11-20 (15). 张志祥.间断动力系统的随机扰动及其在守恒律方程中的应用[D].北京:北京大学数学学院,1998. 冯西桥.核反应堆压力管 道与压力容器的LBB 分析[R].北京:清华大学核能技术设计研究院莫少强.数字式中文全文文献格式的设计与研究[J/OL].情报学报,1999,18(4):https://www.360docs.net/doc/cd15276757.html,/periodical/qbxb/qbxb990407.htm. 奚纪荣,邱志方.武略文韬:军事知识趣谈[M/OL].上海: 汉语大词典出版社, 2001: [13]杜莲.“9·11”事件影响英国出版news/20010929/200109290016.htm. 英文作者姓名全部 用大写字母
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毕业论文（设计）外文翻译 题目：中国上市公司偏好股权融资：非制度性因素 系部名称：经济管理系专业班级:会计082班 学生姓名：任民学号： 200880444228 指导教师：冯银波教师职称:讲师 年月日
译文： 中国上市公司偏好股权融资：非制度性因素 国际商业管理杂志 2009.10 摘要：本文把重点集中于中国上市公司的融资活动，运用西方融资理论，从非制度性因素方面，如融资成本、企业资产类型和质量、盈利能力、行业因素、股权结构因素、财务管理水平和社会文化，分析了中国上市公司倾向于股权融资的原因，并得出结论，股权融资偏好是上市公司根据中国融资环境的一种合理的选择。最后，针对公司的股权融资偏好提出了一些简明的建议。 关键词：股权融资，非制度性因素，融资成本 一、前言 中国上市公司偏好于股权融资，根据中国证券报的数据显示，1997年上市公司在资本市场的融资金额为95.87亿美元，其中股票融资的比例是72.5％，，在1998年和1999年比例分别为72.6％和72.3％，另一方面，债券融资的比例分别是17.8％，24.9％和25.1％。在这三年，股票融资的比例，在比中国发达的资本市场中却在下跌。以美国为例，当美国企业需要的资金在资本市场上，于股权融资相比他们宁愿选择债券融资。统计数据显示，从1970年到1985年，美日企业债券融资占了境外融资的91.7％，比股权融资高很多。阎达五等发现，大约中国3/4的上市公司偏好于股权融资。许多研究的学者认为，上市公司按以下顺序进行外部融资：第一个是股票基金，第二个是可转换债券，三是短期债务，最后一个是长期负债。许多研究人员通常分析我国上市公司偏好股权是由于我们国家的经济改革所带来的制度性因素。他们认为，上市公司的融资活动违背了西方古典融资理论只是因为那些制度性原因。例如，优序融资理论认为，当企业需要资金时，他们首先应该转向内部资金（折旧和留存收益），然后再进行债权融资，最后的选择是股票融资。在这篇文章中，笔者认为，这是因为具体的金融环境激活了企业的这种偏好，并结合了非制度性因素和西方金融理论，尝试解释股权融资偏好的原因。
毕业论文外文翻译模板
农村社会养老保险的现状、问题与对策研究社会保障对国家安定和经济发展具有重要作用，“城乡二元经济”现象日益凸现，农村社会保障问题客观上成为社会保障体系中极为重要的部分。建立和完善农村社会保障制度关系到农村乃至整个社会的经济发展，并且对我国和谐社会的构建至关重要。我国农村社会保障制度尚不完善，因此有必要加强对农村独立社会保障制度的构建，尤其对农村养老制度的改革，建立健全我国社会保障体系。从户籍制度上看，我国居民养老问题可分为城市居民养老和农村居民养老两部分。对于城市居民我国政府已有比较充足的政策与资金投人，使他们在物质和精神方面都能得到较好地照顾，基本实现了社会化养老。而农村居民的养老问题却日益突出，成为摆在我国政府面前的一个紧迫而又棘手的问题。 一、我国农村社会养老保险的现状 关于农村养老，许多地区还没有建立农村社会养老体系，已建立的地区也存在很多缺陷，运行中出现了很多问题，所以完善农村社会养老保险体系的必要性与紧迫性日益体现出来。 （一）人口老龄化加快 随着城市化步伐的加快和农村劳动力的输出，越来越多的农村青壮年人口进入城市，年龄结构出现“两头大，中间小”的局面。中国农村进入老龄社会的步伐日渐加快。第五次人口普查显示：中国65岁以上的人中农村为5938万，占老龄总人口的67.4%.在这种严峻的现实面前，农村社会养老保险的徘徊显得极其不协调。 （二）农村社会养老保险覆盖面太小 中国拥有世界上数量最多的老年人口，且大多在农村。据统计，未纳入社会保障的农村人口还很多，截止2000年底，全国7400多万农村居民参加了保险，占全部农村居民的11.18%，占成年农村居民的11.59%.另外，据国家统计局统计，我国进城务工者已从改革开放之初的不到200万人增加到2003年的1.14亿人。而基本方案中没有体现出对留在农村的农民和进城务工的农民给予区别对待。进城务工的农民既没被纳入到农村养老保险体系中，也没被纳入到城市养老保险体系中，处于法律保护的空白地带。所以很有必要考虑这个特殊群体的养老保险问题。
大学毕业论文---软件专业外文文献中英文翻译
软件专业毕业论文外文文献中英文翻译 Object landscapes and lifetimes Tech nically, OOP is just about abstract data typing, in herita nee, and polymorphism, but other issues can be at least as importa nt. The rema in der of this sect ion will cover these issues. One of the most importa nt factors is the way objects are created and destroyed. Where is the data for an object and how is the lifetime of the object con trolled? There are differe nt philosophies at work here. C++ takes the approach that con trol of efficie ncy is the most importa nt issue, so it gives the programmer a choice. For maximum run-time speed, the storage and lifetime can be determined while the program is being written, by placing the objects on the stack (these are sometimes called automatic or scoped variables) or in the static storage area. This places a priority on the speed of storage allocatio n and release, and con trol of these can be very valuable in some situati ons. However, you sacrifice flexibility because you must know the exact qua ntity, lifetime, and type of objects while you're writing the program. If you are trying to solve a more general problem such as computer-aided desig n, warehouse man ageme nt, or air-traffic con trol, this is too restrictive. The sec ond approach is to create objects dyn amically in a pool of memory called the heap. In this approach, you don't know un til run-time how many objects you n eed, what their lifetime is, or what their exact type is. Those are determined at the spur of the moment while the program is runnin g. If you n eed a new object, you simply make it on the heap at the point that you n eed it. Because the storage is man aged dyn amically, at run-time, the amount of time required to allocate storage on the heap is sig ni fica ntly Ion ger tha n the time to create storage on the stack. (Creat ing storage on the stack is ofte n a si ngle assembly in structio n to move the stack poin ter dow n, and ano ther to move it back up.) The dyn amic approach makes the gen erally logical assumpti on that objects tend to be complicated, so the extra overhead of finding storage and releas ing that storage will not have an importa nt impact on the creati on of an object .In additi on, the greater flexibility is esse ntial to solve the gen eral program ming problem. Java uses the sec ond approach, exclusive". Every time you want to create an object, you use the new keyword to build a dyn amic in sta nee of that object. There's ano ther issue, however, and that's the lifetime of an object. With Ian guages that allow objects to be created on the stack, the compiler determines how long the object lasts and can automatically destroy it. However, if you create it on the heap the compiler has no kno wledge of its lifetime. In a Ianguage like C++, you must determine programmatically when to destroy the
电子信息工程专业毕业论文外文翻译中英文对照翻译
本科毕业设计（论文）中英文对照翻译 院（系部）电气工程与自动化 专业名称电子信息工程 年级班级 04级7班 学生姓名 指导老师
Infrared Remote Control System Abstract Red outside data correspondence the technique be currently within the scope of world drive extensive usage of a kind of wireless conjunction technique,drive numerous hardware and software platform support. Red outside the transceiver product have cost low, small scaled turn, the baud rate be quick, point to point SSL, be free from electromagnetism thousand Raos etc.characteristics, can realization information at dissimilarity of the product fast, convenience, safely exchange and transmission, at short distance wireless deliver aspect to own very obvious of advantage.Along with red outside the data deliver a technique more and more mature, the cost descend, red outside the transceiver necessarily will get at the short distance communication realm more extensive of application. The purpose that design this system is transmit cu stomer’s operation information with infrared rays for transmit media, then demodulate original signal with receive circuit. It use coding chip to modulate signal and use decoding chip to demodulate signal. The coding chip is PT2262 and decoding chip is PT2272. Both chips are made in Taiwan. Main work principle is that we provide to input the information for the PT2262 with coding keyboard. The input information was coded by PT2262 and loading to high frequent load wave whose frequent is 38 kHz, then modulate infrared transmit dioxide and radiate space outside when it attian enough power. The receive circuit receive the signal and demodulate original information. The original signal was decoded by PT2272, so as to drive some circuit to accomplish
毕业论文外文资料翻译
毕业论文外文资料翻译题目(宋体三号，居中) 学院(全称，宋体三号，居中) 专业(全称，宋体三号，居中) 班级(宋体三号，居中) 学生(宋体三号，居中) 学号(宋体三号，居中) 指导教师(宋体三号，居中) 二〇一〇年月日(宋体三号，居中，时间与开题时间一致)
（英文原文装订在前）
Journal of American Chemical Society, 2006, 128(7): 2421-2425. (文献翻译必须在中文译文第一页标明文献出处：即文章是何期刊上发表的，X年X 卷X期，格式如上例所示，四号，右对齐，杂志名加粗。) [点击输入译文题目-标题1，黑体小二] [点击输入作者，宋体小四] [点击输入作者单位，宋体五号] 摘要[点击输入，宋体五号] 关键词[点击输入，宋体五号] 1[点击输入一级标题-标题2，黑体四号] [点击输入正文，宋体小四号，1.25倍行距] 1.1[点击输入二级标题-标题3，黑体小四] [点击输入正文，宋体小四，1.25倍行距] 1.1.1[点击输入三级标题-标题4，黑体小四] [点击输入正文，宋体小四，1.25倍行距] 说明： 1.外文文章必须是正规期刊发表的。 2.翻译后的中文文章必须达到2000字以上，并且是一篇完整文章。 3.必须要有外文翻译的封面，使用学校统一的封面； 封面上的翻译题目要写翻译过来的中文题目； 封面上时间与开题时间一致。 4.外文原文在前，中文翻译在后； 5.中文翻译中要包含题目、摘要、关键词、前言、全文以及参考文献，翻译要条理
清晰，中文翻译要与英文一一对应。 6.翻译中的中文文章字体为小四，所有字母、数字均为英文格式下的，中文为宋体， 标准字符间距。 7.原文中的图片和表格可以直接剪切、粘贴，但是表头与图示必须翻译成中文。 8.图表必须居中，文章段落应两端对齐、首行缩进2个汉字字符、1.25倍行距。 例如： 图1. 蛋白质样品的PCA图谱与8-卟啉识别排列分析(a)或16-卟啉识别排列分析(b)。为了得到b 的 数据矩阵，样品用16-卟啉识别排列分析来检测，而a 是通过捕获首八卟啉接收器数据矩阵从 b 中 萃取的。
本科毕业设计外文翻译(原文)
Real-time interactive optical micromanipulation of a mixture of high- and low-index particles Peter John Rodrigo, Vincent Ricardo Daria and Jesper Glückstad Optics and Plasma Research Department, Ris? National Laboratory, DK-4000 Roskilde, Denmark jesper.gluckstad@risoe.dk http://www.risoe.dk/ofd/competence/ppo.htm Abstract: We demonstrate real-time interactive optical micromanipulation of a colloidal mixture consisting of particles with both lower (n L < n0) and higher (n H > n0) refractive indices than that of the suspending medium (n0). Spherical high- and low-index particles are trapped in the transverse plane by an array of confining optical potentials created by trapping beams with top-hat and annular cross-sectional intensity profiles, respectively. The applied method offers extensive reconfigurability in the spatial distribution and individual geometry of the optical traps. We experimentally demonstrate this unique feature by simultaneously trapping and independently manipulating various sizes of spherical soda lime micro- shells (n L≈ 1.2) and polystyrene micro-beads (n H = 1.57) suspended in water (n0 = 1.33). ?2004 Optical Society of America OCIS codes: (140.7010) Trapping, (170.4520) Optical confinement and manipulation and (230.6120) Spatial Light Modulators. References and links 1. A. Ashkin, “Optical trapping and manipulation of neutral particles using lasers,” Proc. Natl. Acad. Sci. USA 94, 4853-4860 (1997). 2. K. Svoboda and S. M. Block, “Biological applications of optical forces,” Annu. Rev. Biophys. Biomol. Struct. 23, 247-285 (1994). 3. D. G. Grier, “A revolution in optical manipulation,” Nature 424, 810-816 (2003). 4. M. P. MacDonald, G. C. Spalding and K. Dholakia, “Microfluidic sorting in an optical lattice,” Nature 426, 421-424 (2003). 5. J. Glückstad, “Microfluidics: Sorting particles with light,” Nature Materials 3, 9-10 (2004). 6. A. Ashkin, “Acceleration and trapping of particles by radiation-pressure,”Phys. Rev. Lett. 24, 156-159 (1970). 7. A. Ashkin, J. M. Dziedzic, J. E. Bjorkholm and S. Chu, “Observation of a single-beam gradient force optical trap for dielectric particles,” Opt. Lett. 11, 288-290 (1986). 8. K. Sasaki, M. Koshioka, H. Misawa, N. Kitamura, and H. Masuhara, “Optical trapping of a metal particle and a water droplet by a scanning laser beam,” Appl. Phys. Lett. 60, 807-809 (1992). 9. K. T. Gahagan and G. A. Swartzlander, “Trapping of low-index microparticles in an optical vortex,” J. Opt. Soc. Am. B 15, 524-533 (1998). 10. K. T. Gahagan and G. A. Swartzlander, “Simultaneous trapping of low-index and high-index microparticles observed with an optical-vortex trap,” J. Opt. Soc. Am. B 16, 533 (1999). 11. M. P. MacDonald, L. Paterson, W. Sibbett, K. Dholakia, P. Bryant, “Trapping and manipulation of low-index particles in a two-dimensional interferometric optical trap,” Opt. Lett. 26, 863-865 (2001). 12. R. L. Eriksen, V. R. Daria and J. Glückstad, “Fully dynamic multiple-beam optical tweezers,” Opt. Express 10, 597-602 (2002), https://www.360docs.net/doc/cd15276757.html,/abstract.cfm?URI=OPEX-10-14-597. 13. P. J. Rodrigo, R. L. Eriksen, V. R. Daria and J. Glückstad, “Interactive light-driven and parallel manipulation of inhomogeneous particles,” Opt. Express 10, 1550-1556 (2002), https://www.360docs.net/doc/cd15276757.html,/abstract.cfm?URI=OPEX-10-26-1550. 14. V. Daria, P. J. Rodrigo and J. Glückstad, “Dynamic array of dark optical traps,” Appl. Phys. Lett. 84, 323-325 (2004). 15. J. Glückstad and P. C. Mogensen, “Optimal phase contrast in common-path interferometry,” Appl. Opt. 40, 268-282 (2001). 16. S. Maruo, K. Ikuta and H. Korogi, “Submicron manipulation tools driven by light in a liquid,” Appl. Phys. Lett. 82, 133-135 (2003). #3781 - $15.00 US Received 4 February 2004; revised 29 March 2004; accepted 29 March 2004 (C) 2004 OSA 5 April 2004 / Vol. 12, No. 7 / OPTICS EXPRESS 1417
电气专业毕业论文外文翻译分析解析
本科毕业设计 外文文献及译文 文献、资料题目：Designing Stable Control Loops 文献、资料来源：期刊 文献、资料发表（出版）日期：2010.3.25 院（部）：信息与电气工程学院 专班姓学业：电气工程与自动化级： 名： 号： 指导教师：翻译日期：2011.3.10
外文文献： Designing Stable Control Loops The objective of this topic is to provide the designer with a practical review of loop compensation techniques applied to switching power supply feedback control. A top-down system approach is taken starting with basic feedback control concepts and leading to step-by-step design procedures,initially applied to a simple buck regulator and then expanded to other topologies and control algorithms. Sample designs are demonstrated with Math cad simulations to illustrate gain and phase margins and their impact on performance analysis. I. I NTRODUCTION Insuring stability of a proposed power supply solution is often one of the more challenging aspects of the design process. Nothing is more disconcerting than to have your lovingly crafted breadboard break into wild oscillations just as its being demonstrated to the boss or customer, but insuring against this unfortunate event takes some analysis which many designers view as formidable. Paths taken by design engineers often emphasize either cut-and-try empirical testing in the laboratory or computer simulations looking for numerical solutions based on complex mathematical models.While both of these approach a basic understanding of feedback theory will usually allow the definition of an acceptable compensation network with a minimum of computational effort. II. S TABILITY D EFINED Fig. 1.Definition of stability Fig. 1 gives a quick illustration of at least one definition of stability. In its simplest terms, a system is stable if, when subjected to a perturbation from some source, its response to that