《计算机网络》
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Speech recognition
History
One of the most notable domains for the commercial application of speech recognition in the United States has been health care and in particular the work of the . According to industry experts, at its inception, speech recognition (SR) was sold as a way to completely eliminate transcription rather than make the transcription process more efficient, hence it was not accepted. It was also the case that SR at that time was often technically deficient. Additionally, to be used effectively, it required changes to the ways physicians worked and documented clinical encounters, which many if not all were reluctant to do. The biggest limitation to speech recognition automating transcription, however, is seen as the software. The nature of narrative dictation is highly interpretive and often requires judgment that may be provided by a real human but not yet by an automated system. Another limitation has been the extensive amount of time required by the user and/or system provider to train the software.
A distinction in ASR is often made between "artificial syntax systems" which are usually domain-specific and "natural language processing" which is usually language-specific. Each of these types of application presents its own particular goals and challenges. Applications
Health care
In the domain, even in the wake of improving speech recognition technologies, medical transcriptionists (MTs) have not yet become obsolete. Many experts in the field anticipate that with increased use of speech recognition technology, the services provided may be redistributed rather than replaced.
Speech recognition can be implemented in front-end or back-end of the medical
documentation process.
Front-End SR is where the provider dictates into a speech-recognition engine, the
recognized words are displayed right after they are spoken, and the dictator is responsible for editing and signing off on the document. It never goes through an editor.
Back-End SR is where the provider dictates into a digital dictation system, and the voice is routed through a speech-recognition machine and the recognized draft document is routed - 2 -
along with the original voice file to the editor, who edits the draft and finalizes the report. Deferred SR is being widely used in the industry currently.
Many (EMR) applications can be more effective and may be performed more easily when deployed in conjunction with a speech-recognition engine. Searches, queries, and form filling may all be faster to perform by voice than by using a keyboard.
Military
High-performance fighter aircraft
Substantial efforts have been devoted in the last decade to the test and evaluation of speech recognition in fighter aircraft. Of particular note are the U.S. program in speech
recognition for the Advanced Fighter Technology Integration (AFTI)/F-16 aircraft (F-16 VISTA), the program in France on installing speech recognition systems on Mirage aircraft, and programs in the UK dealing with a variety of aircraft platforms. In these programs, speech recognizers have been operated successfully in fighter aircraft with applications including: setting radio frequencies, commanding an autopilot system, setting steer-point coordinates and weapons release parameters, and controlling flight displays. Generally,
only very limited, constrained vocabularies have been used successfully, and a major effort has been devoted to integration of the speech recognizer with the avionics system.
Some important conclusions from the work were as follows:
1. Speech recognition has definite potential for reducing pilot workload, but this
potential was not realized consistently.
2. Achievement of very high recognition accuracy (95% or more) was the most
critical factor for making the speech recognition system useful — with lower
recognition rates, pilots would not use the system.
3. More natural vocabulary and grammar, and shorter training times would be useful,
but only if very high recognition rates could be maintained.
Laboratory research in robust speech recognition for military environments has produced promising results which, if extendable to the cockpit, should improve the utility of speech recognition in high-performance aircraft.
Working with Swedish pilots flying in the JAS-39 Gripen cockpit, Englund (2004) found recognition deteriorated with increasing G-loads. It was also concluded that adaptation greatly improved the results in all cases and introducing models for breathing was shown to improve recognition scores significantly. Contrary to what might be expected, no effects of the broken English of the speakers were found. It was evident that spontaneous speech caused problems for the recognizer, as could be expected. A restricted vocabulary, and
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above all, a proper syntax, could thus be expected to improve recognition accuracy
substantially.
The Eurofighter Typhoon currently in service with the UK RAF employs a
speaker-dependent system, i.e. it requires each pilot to create a template. The system is not used for any safety critical or weapon critical tasks, such as weapon release or lowering of the undercarriage, but is used for a wide range of other cockpit functions. Voice commands are confirmed by visual and/or aural feedback. The system is seen as a major design feature in the reduction of pilot workload, and even allows the pilot to assign targets to himself with two simple voice commands or to any of his wingmen with only five
commands.
Helicopters
The problems of achieving high recognition accuracy under stress and noise pertain
strongly to the helicopter environment as well as to the fighter environment. The acoustic noise problem is actually more severe in the helicopter environment, not only because of the high noise levels but also because the helicopter pilot generally does not wear a facemask, which would reduce acoustic noise in the microphone. Substantial test and evaluation programs have been carried out in the past decade in speech recognition systems applications in helicopters, notably by the U.S. Army Avionics Research and Development Activity (AVRADA) and by the Royal Aerospace Establishment (RAE) in the UK. Work in France has included speech recognition in the Puma helicopter. There has also been much useful work in Canada. Results have been encouraging, and voice
applications have included: control of communication radios; setting of navigation systems; and control of an automated target handover system.
As in fighter applications, the overriding issue for voice in helicopters is the impact on
pilot effectiveness. Encouraging results are reported for the AVRADA tests, although these represent only a feasibility demonstration in a test environment. Much remains to be done both in speech recognition and in overall speech recognition technology, in order to consistently achieve performance improvements in operational settings.
Battle management
Battle management command centres generally require rapid access to and control of large, rapidly changing information databases. Commanders and system operators need to query these databases as conveniently as possible, in an eyes-busy environment where much of the information is presented in a display format. Human machine interaction by voice has the potential to be very useful in these environments. A number of efforts have been undertaken to interface commercially available isolated-word recognizers into battle - 4 -
management environments. In one feasibility study, speech recognition equipment was tested in conjunction with an integrated information display for naval battle management applications. Users were very optimistic about the potential of the system, although capabilities were limited.
Speech understanding programs sponsored by the Defense Advanced Research Projects Agency (DARPA) in the U.S. has focused on this problem of natural speech interface.. Speech recognition efforts have focused on a database of continuous speech recognition (CSR), large-vocabulary speech which is designed to be representative of the naval
resource management task. Significant advances in the state-of-the-art in CSR have been achieved, and current efforts are focused on integrating speech recognition and natural language processing to allow spoken language interaction with a naval resource
management system.
Training air traffic controllers
Training for military (or civilian) air traffic controllers (ATC) represents an excellent
application for speech recognition systems. Many ATC training systems currently require a person to act as a "pseudo-pilot", engaging in a voice dialog with the trainee controller, which simulates the dialog which the controller would have to conduct with pilots in a real ATC situation. Speech recognition and synthesis techniques offer the potential to eliminate the need for a person to act as pseudo-pilot, thus reducing training and support personnel. Air controller tasks are also characterized by highly structured speech as the primary output of the controller, hence reducing the difficulty of the speech recognition task.
The U.S. Naval Training Equipment Center has sponsored a number of developments of prototype ATC trainers using speech recognition. Generally, the recognition accuracy falls short of providing graceful interaction between the trainee and the system. However, the prototype training systems have demonstrated a significant potential for voice interaction in these systems, and in other training applications. The U.S. Navy has sponsored a
large-scale effort in ATC training systems, where a commercial speech recognition unit was integrated with a complex training system including displays and scenario creation. Although the recognizer was constrained in vocabulary, one of the goals of the training programs was to teach the controllers to speak in a constrained language, using specific vocabulary specifically designed for the ATC task. Research in France has focused on the application of speech recognition in ATC training systems, directed at issues both in speech recognition and in application of task-domain grammar constraints.
The USAF, USMC, US Army, and FAA are currently using ATC simulators with speech recognition from a number of different vendors, including UFA, Inc. , and Adacel Systems Inc (ASI). This software uses speech recognition and synthetic speech to enable the trainee to control aircraft and ground vehicles in the simulation without the need for pseudo pilots.
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Another approach to ATC simulation with speech recognition has been created by Supremis. The Supremis system is not constrained by rigid grammars imposed by the underlying limitations of other recognition strategies.
Telephony and other domains
ASR in the field of telephony is now commonplace and in the field of computer gaming and simulation is becoming more widespread. Despite the high level of integration with word processing in general personal computing, however, ASR in the field of document production has not seen the expected increases in use.
The improvement of mobile processor speeds made feasible the speech-enabled Symbian and Windows Mobile Smartphones. Current speech-to-text programs are too large and
require too much CPU power to be practical for the Pocket PC. Speech is used mostly as a part of User Interface, for creating pre-defined or custom speech commands. Leading software vendors in this field are: Microsoft Corporation (Microsoft Voice Command); Nuance Communications (Nuance Voice Control); Vito Technology (VITO Voice2Go); Speereo Software (Speereo Voice Translator). MyCaption for BlackBerry .
People with Disabilities
People with disabilities are another part of the population that benefit from using speech recognition programs. It is especially useful for people who have difficulty with or are unable to use their hands, from mild repetitive stress injuries to involved disabilities that require alternative input for support with accessing the computer. In fact, people who used the keyboard a lot and developed became an urgent early market for speech
recognition. Speech recognition is used in , such as voice-to-text voicemail, , and . Individuals with learning disabilities who have problems with thought to paper communication (essentially they think of an idea but it is processed incorrectly causing it to end up differently on paper) can benefit from the software as it helps to overlap that weakness.
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语音识别
发展历史
商业应用的语音识别在美国是保健和medical transcriptionist等最显著的领域之一 。据业界专家称，在其成立以来，语音识别（简称SR）是为了彻底消除转录，而不是使转录过程更有效率，因此是不能接受的。也有人认为，SR在那个时候往往是在技术上缺乏。此外，为了有效地加以利用，它需要改变工作方式和记录医师临床实践，其中甚至有许多不是所有的人愿意做的事。然而，语音识别自动化转录，被看作是该软件最大的限制。记叙一件事的本质是在听写时非常需要解释和判断，这需要一个真正的人，而不是通过自动化系统。另一个限制是用户和或系统供应商，培训软件所需的时间普遍的比较长。
ASR之间的区分往往是特定领域的“人工语法系统”和特定于语言的“自然语言处理”。所有这些类型的应用都有自己的特定目标和挑战。
应用
卫生保健
在卫生保健领域，即使语音识别技术不断改进，medical transcriptionists
（ MTS ）还是不会过时。许多该领域的专家预测，随着语音识别技术的广泛使用，MTS提供的服务可能会重新分配，而不是取代。语音识别可以在医疗文件的进程前端或后端实施。
SR的前端是要求供应商装入语音识别引擎，在他们说完话后正确显示出来，使用者负责编辑，并签署了有关文件。它从未审阅编辑。
后端SR是提供者装入一个数字听写转换系统的地方，声音通过语言识别机器寻址，并且被认可的草案与原始的声音文件一起给编辑者，编辑草稿并且完成报告。 延期的SR是在当前产业用途广泛。
当部署与语言识别引擎一道，许多电子病历(EMR)应用是更加有效的，并且也更加容易地执行。查寻、询问和填表全部由声音执行比通过使用键盘更快速。
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军事
高性能战斗机
在过去十年中我们都花了大量的努力来测试和评价语音识别的战斗机。特别值得注意的是，美国计划为先进战斗机（ AFTI ） / F - 16战斗机（ F - 16型Vista ）安装语音识别的集成技术，法国也计划在幻影飞机安装语音识别系统，英国计划安装在不同的飞机平台。这些程序中，成功在战斗机中应用语音识别的包括：设置无线电频率，指挥一个自动系统，设置引导点坐标和武器释放参数，并控制飞行显示器。通常，只非常顺利地使用了有限，拘束的词汇量，并且主要努力致力于语音识别与航空电子学系统的综合化。
一些重要结论的工作如下：
1.语音识别具有一定的潜力，减少飞行员的工作量，但这种潜力始终没能实现。
2.实现非常高的识别率（ 95 ％或以上）是使语音识别系统有用和识别速率降低的最关键因素，飞行员不会使用该系统。
3.更自然的词汇和语法，和更短的培训时间将是有益的，但前提是非常高的识别率可维持不变。
实验室研究的在军事环境中抗噪语音识别产生了可喜的成果，如果扩展到驾驶舱，应能改进应用语音识别的高性能飞机。
在JAS-39 Gripen驾驶舱内的瑞典飞行员 Englund (2004)发现了公认恶化随着G过载。 也结束适应在所有的情况下很大地改进了结果，并且介绍呼吸的模型显示极大改进公认比分。相反，可以预期，没有任何作用的蹩脚的英语发言者被发现。 很明显，可以预期，造成问题的自发讲话的语音识别。 受限的词汇，最重要的是，一个适当的语法，能因而期望极大地改进公认准确性。
目前在英国皇家空军拥有扬声器依赖系统欧洲台风，它要求每个试点创建一个模板。该系统不作任何安全关键或核武器的关键任务，如核武器释放或降低脚架，但广泛应用于其他驾驶舱职能。语音指令通过视觉和听觉反馈来确认。该系统被看作是一个主要的设计功能，减少飞行员的工作量，甚至可以以自己的两个简单的语音命令，或任何与他的飞行员只有五个命令改变试点的目标。 8
直升机
在高压和强烈噪音干扰的直升机环境实现高识别率的问题和战斗机环境一眼好。实际上，在直升机的环境，噪音问题更为严重，不仅是因为高噪音水平，而且还因为，直升机飞行员一般不戴减少麦克风噪音的口罩。语音识别系统在过去十年中已经进行了大量的试验和评估程序并应用在直升机，特别是美国陆军航空电子研究和发展活动（ AVRADA ）和英国的皇家航空建立（评审）。在法国的美洲豹直升机也安装了语音识别系统。在加拿大也有许多有益的工作。结果令人鼓舞，语音应用包括：无线电通信控制;设置的导航系统;和控制的自动化目标移交制度。
如在战斗机的应用，首要的问题是直升机的噪声影响。令人鼓舞的结果报告了AVRADA测试，虽然这些只是在测试环境论证可行性。仍然要在语音识别和语音识别技术的整体做许多工作，以便不断获得性能方面和操作的设置的改进。
战斗管理
战斗管理指挥中心通常需要快速获得和控制迅速变化的信息数据库。指挥官和系统运营商需要在许多资料显示让人眼花缭乱的环境中尽可能方便查询这些数据库。人机互动语音有可能是在这些环境中非常有用的。一些在商业化的界面进行的孤立词识别已经投入战斗管理环境。在一个可行性研究中，语音识别设备在海战管理应用中进行了测试与综合信息显示。用户对它的潜力非常乐观，虽然能力是有限的。
由美国国防部高级研究计划局（ DARPA ）负责的言语理解项目在美国集中于自然演讲界面的这个问题。语音识别的努力集中于一个数据库的连续语音识别（ CSR公司） ，大词汇量的讲话，其目的是要代表海军资源管理的任务。最先进的CSR公司已经实现重大突破，当前的努力集中于集成语音识别和自然语言处理，使口语互动海军的资源管理系统。
空中交通管制员的培训
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培训军事（或平民）空中交通管制（空管）是对语音识别系统的一个极好的应用。许多区域交通控制系统，目前需要培训的人充当“假试点” ，进行语音对话与实习控制器，模拟对话的控制器将不得不与飞行员进行真正的区域交通控制局势。语音识别和合成技术提供的潜力，以消除需要一个人作为伪试点，从而减少培训和技术支持人员。空调控制器任务描绘的也是为高度被构造的讲话作为控制器的主要产品，因此减少语音识别任务的困难。
使用语音识别，美国海军训练设备中心主办了原型ATC教练员的一定数量的发展。 通常，缺乏准确性的公认提供实习生和系统之间的优美的互作用。 然而，原型培训系统展示了在声音互作用的重大潜力在这些系统和在其他训练应用。 美国海军主办了在ATC培训系统的大规模努力，一个商业语音识别单位集成与复杂培训系统包括显示和情景创作。 虽然识别器在词汇量被压抑了，其中一个训练计划的目标是教控制器讲话在一种拘束的语言，使用为ATC任务明确地设计的具体词汇量。 研究在法国集中于语音识别的应用在ATC培训系统的，被指挥在问题在语音识别和在任务领域语法限制的应用。
美国空军， USMC ，美国陆军，和美国联邦航空局目前使用的空管模拟器与语音识别是从若干不同的厂商购买的，包括乌发公司和Adacel系统公司（意） 。该软件使用语音识别与合成的讲话，使学员来控制飞机和地面车辆的模拟，而不需要伪飞行员。
空管模拟语音识别是由Supremis创造了的。 Supremis系统没有由其他公认战略的部下的局限强加的刚性语法。
电话和其他领域
自动语音识别领域中的电话现在是司空见惯的，并在该领域的计算机游戏和模拟正变得越来越普遍。尽管一般个人电脑都要高集成度文字处理的，但是，自动语音识别领域中的文件制作还没有看到预期的增加使用。
移动处理器速度的改善做出了可行的语音功能Symbian和Windows Mobile智能手机。目前语音转文字的程序过大，实际的掌上电脑需要太多的CPU功率。讲话，是主要用于为部分用户界面，用于创建预先定义或自定义语音命令。在这一领域领先的软件供应商是：微软公司（微软语音命令）；细微通信（细微语音10
控制） ；维托技术（超级Voice2Go ）；Speereo软件（ Speereo语音翻译）；MyCaption黑莓。
残疾人士
残疾人是另一个部分受益于使用语音识别程序的群体。这是特别有用的人谁有困难或无法使用他们的手，特别是轻微受伤涉及残疾人，需要替代的投入支持与访问计算机。事实上大量使用键盘和发展RSI的人成为早期紧迫的言语识别的市场。语音识别是聋哑人使用的电话，如spinvox语音到文本语音中继服务，并配上电话。有学习障碍的问题的个人也认为，文件通信（主要是他们认为的想法，它是因处理不当造成提前结束的不同的文件） ，将受益于软件，因为它有助于重叠的弱点。
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