- •Лекционный комплекс
- •1. The beginning
- •2. The early years
- •3. The 1960s, the alpac report and the seventies
- •4. The 1980s and early 1990s
- •5. Recent research
- •2. Dictionary software
- •3. Handheld dictionaries or peDs
- •4. Online dictionaries
- •2. Functions of a translation memory
- •3. Centralized translation memory
- •1. Hutchins, j.H. And h. Somers: Machine Translation. Academic Press, 1992.
- •2. Hovy, e.H. Overview article in mitecs (mit Encyclopedia of the Cognitive Sciences). 1998.
- •3. Hovy, e.H. Review in byte magazine, January 1993.
- •4. Knight, k. 1997. Automating Knowledge Acquisition for Machine Translation. Ai Magazine 18(4), (81–95).
- •1. History of speech translation
- •2. Features of speech translation
- •3. Challenges and future prospects
- •1. Hutchins, j.H. And h. Somers: Machine Translation. Academic Press, 1992.
- •2. Hovy, e.H. Overview article in mitecs (mit Encyclopedia of the Cognitive Sciences). 1998.
- •3. Hovy, e.H. Review in byte magazine, January 1993.
- •4. Knight, k. 1997. Automating Knowledge Acquisition for Machine Translation. Ai Magazine 18(4), (81–95).
- •1. Hutchins, j.H. And h. Somers: Machine Translation. Academic Press, 1992.
- •2. Hovy, e.H. Overview article in mitecs (mit Encyclopedia of the Cognitive Sciences). 1998.
- •3. Hovy, e.H. Review in byte magazine, January 1993.
- •4. Knight, k. 1997. Automating Knowledge Acquisition for Machine Translation. Ai Magazine 18(4), (81–95).
- •1. Hutchins, j.H. And h. Somers: Machine Translation. Academic Press, 1992.
- •2. Hovy, e.H. Overview article in mitecs (mit Encyclopedia of the Cognitive Sciences). 1998.
- •3. Hovy, e.H. Review in byte magazine, January 1993.
- •4. Knight, k. 1997. Automating Knowledge Acquisition for Machine Translation. Ai Magazine 18(4), (81–95).
- •1. Hutchins, j.H. And h. Somers: Machine Translation. Academic Press, 1992.
- •1. Hutchins, j.H. And h. Somers: Machine Translation. Academic Press, 1992.
- •2. Hovy, e.H. Overview article in mitecs (mit Encyclopedia of the Cognitive Sciences). 1998.
- •3. Hovy, e.H. Review in byte magazine, January 1993.
- •4. Knight, k. 1997. Automating Knowledge Acquisition for Machine Translation. Ai Magazine 18(4), (81–95).
- •1. Hutchins, j.H. And h. Somers: Machine Translation. Academic Press, 1992.
- •2. Hovy, e.H. Overview article in mitecs (mit Encyclopedia of the Cognitive Sciences). 1998.
- •3. Hovy, e.H. Review in byte magazine, January 1993.
- •4. Knight, k. 1997. Automating Knowledge Acquisition for Machine Translation. Ai Magazine 18(4), (81–95).
- •2. The notion of sublanguage
- •1. Hutchins, j.H. And h. Somers: Machine Translation. Academic Press, 1992.
- •2. Hovy, e.H. Overview article in mitecs (mit Encyclopedia of the Cognitive Sciences). 1998.
- •3. Hovy, e.H. Review in byte magazine, January 1993.
- •4. Knight, k. 1997. Automating Knowledge Acquisition for Machine Translation. Ai Magazine 18(4), (81–95).
- •1. Situational (denotative) model of translation
- •2. Transformational model of translation
- •3. Psycholinguistic model of translation.
- •1. Situational model of translation
- •2. Transformational model of translation
- •3. Psycholinguistic model of translation
- •1. Hutchins, j.H. And h. Somers: Machine Translation. Academic Press, 1992.
- •3. Forums for translators.
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2. Features of speech translation
Apart from the problems involved in the text translation, it also has to deal with special problems occur in speech-to-speech translation, incorporating incoherence of spoken language, fewer grammar constraints of spoken language, unclear word boundary of spoken language, the correction of speech recognition errors and multiple optional inputs. Additionally, speech-to-speech translation also has its advantages compared with text translation, including less complex structure of spoken language and less vocabulary in spoken language.
Standards
When many countries begin to research and develop speech translation, it will be necessary to standardize interfaces and data formats to ensure that the systems are mutually compatible. International joint research is being fostered by speech translation consortiums (e.g. the C-STAR international consortium for joint research of speech translation and A-STAR for the Asia-Pacific region). They were founded as “international joint-research organizations to design formats of bilingual corpora that are essential to advance the research and development of this technology and to standardize interfaces and data formats to connect speech translation module internationally”
Applications
Today, speech translation systems are being used throughout the world. Examples include medical facilities, schools, police, hotels, retail stores, and factories. These systems are applicable anywhere that spoken language is being used to communicate. A popular application is Jibbigo that works offline.
3. Challenges and future prospects
Currently, speech translation technology is available as product that instantly translates free form multi-lingual conversations. These systems instantly translate continuous speech. Challenges in accomplishing this include overcoming Speaker dependent variations in style of speaking or pronunciation are issues that have to be dealt with in order to provide high quality translation for all users. Moreover, speech recognition systems must be able to remedy external factors such as acoustic noise or speech by other speakers in real-world use of speech translation systems.
For the reason that the user does not understand the target language when speech translation is used, a method "must be provided for the user to check whether the translation is correct, by such means as translating it again back into the user's language". In order to achieve the goal of erasing the language barrier world wide, multiple languages have to be supported. This requires speech corpora, bilingual corpora and text corpora for each of the estimated 6,000 languages said to exist on our planet today.
As the collection of corpora is extremely expensive, collecting data from the Web would be an alternative to conventional methods. “Secondary use of news or other media published in multiple languages would be an effective way to improve performance of speech translation.” However, “current copyright law does not take secondary uses such as these types of corpora into account” and thus “it will be necessary to revise it so that it is more flexible.”
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