Abstract: A hybrid speech translation system whereby a wireless-enabled client computing device can, in an offline mode, translate input speech utterances from one language to another locally, and also, in an online mode when there is wireless network connectivity, have a remote computer perform the translation and transmit it back to the client computing device via the wireless network for audible outputting by client computing device. The user of the client computing device can transition between modes or the transition can be automatic based on user preferences or settings. The back-end speech translation server system can adapt the various recognition and translation models used by the client computing device in the offline mode based on analysis of user data over time, to thereby configure the client computing device with scaled-down, yet more efficient and faster, models than the back-end speech translation server system, while still be adapted for the user's domain.

Abstract: An improved lecture support system integrates multi-media presentation materials with spoken content so that the listener can follow with both the speech and the supporting materials that accompany the presentation to provide additional understanding. Computer-based systems and methods are disclosed for translation of a spoken presentation (e.g., a lecture, a video) along with the accompanying presentation materials. The content of the presentation materials can be used to improve presentation translation, as it extracts supportive material from the presentation materials as they relate to the speech.

Abstract: Computer-implemented systems and methods for extracting information during a human-to-human mono-lingual or multi-lingual dialog between two speakers are disclosed. Information from either the recognized speech (or the translation thereof) by the second speaker and/or the recognized speech by the first speaker (or the translation thereof) is extracted. The extracted information is then entered into an electronic form stored in a data store.

Abstract: A social networking system determines whether a particular user is qualified to provide translations of text from a first language to a second language. The determination may include evaluation of the language competencies of the user, and also of the trustworthiness of the user as a translator, as determined based on prior translations submitted by the user. The social networking system also selects translations of a text item for a user to whom that text is to be shown. When evaluating a candidate translation for presentation to the user, the evaluation may assess factors such as the determined qualification as a translator of the user who provided the candidate translation; a quality score of the candidate translation itself; and/or the similarity of the user viewing the content and the user providing the candidate translation.

Abstract: A social networking system obtains user pronunciations of terms whose pronunciations might vary among different users, such as names of users. The social networking system additionally obtains demographic information about the users from whom the pronunciations were obtained, as well as social graph information for those users, such as information about connections of those users in the social graph. Based on the obtained pronunciations, the demographic information, and the social graph information, the social networking system determines, for a user having that name (or other term in question), one or more suggested pronunciations for the name that are likely to be the pronunciations that that user would use.

Abstract: An iterative language translation system includes multiple communicatively connected statistical speech translation systems. The system includes an automatic speech recognition component adapted to recognize spoken language in a source language and to create a source language hypothesis. A machine translation component is adapted to translate the source language hypothesis into a target language. The system also includes a second automatic speech recognition component and second machine translation component. The translation results are used to adapt the automatic speech recognition components and the language hypotheses are used to adapt the machine translation components.

Abstract: A social networking system determines whether a particular user is qualified to provide translations of text from a first language to a second language. The determination may include evaluation of the language competencies of the user, and also of the trustworthiness of the user as a translator, as determined based on prior translations submitted by the user. The social networking system also selects translations of a text item for a user to whom that text is to be shown. When evaluating a candidate translation for presentation to the user, the evaluation may assess factors such as the determined qualification as a translator of the user who provided the candidate translation; a quality score of the candidate translation itself; and/or the similarity of the user viewing the content and the user providing the candidate translation.

Abstract: Technology is disclosed for mining training data to create machine translation engines. Training data can be mined as translation pairs from single content items that contain multiple languages; multiple content items in different languages that are related to the same or similar target; or multiple content items that are generated by the same author in different languages. Locating content items can include identifying potential sources of translation pairs that fall into these categories and applying filtering techniques to quickly gather those that are good candidates for being actual translation pairs. When actual translation pairs are located, they can be used to retrain a machine translation engine as in-domain for social media content items.

Abstract: A social networking system determines whether a particular user is qualified to provide translations of text from a first language to a second language. The determination may include evaluation of the language competencies of the user, and also of the trustworthiness of the user as a translator, as determined based on prior translations submitted by the user. The social networking system also selects translations of a text item for a user to whom that text is to be shown. When evaluating a candidate translation for presentation to the user, the evaluation may assess factors such as the determined qualification as a translator of the user who provided the candidate translation; a quality score of the candidate translation itself; and/or the similarity of the user viewing the content and the user providing the candidate translation.

Abstract: An improved lecture support system integrates multi-media presentation materials with spoken content so that the listener can follow with both the speech and the supporting materials that accompany the presentation to provide additional understanding. Computer-based systems and methods are disclosed for translation of a spoken presentation (e.g., a lecture, a video) along with the accompanying presentation materials. The content of the presentation materials can be used to improve presentation translation, as it extracts supportive material from the presentation materials as they relate to the speech.

Abstract: A speech translation system and methods for cross-lingual communication that enable users to improve and customize content and usage of the system and easily. The methods include, in response to receiving an utterance including a first term associated with a field, translating the utterance into a second language. In response to receiving an indication to add the first term associated with the field to a first recognition lexicon, adding the first term associated with the field and the determined translation to a first machine translation module and to a shared database for a community associated with the field of the first term associated with the field, wherein the first term associated with the field added to the shared database is accessible by the community.

Abstract: Technology is disclosed for mining training data to create machine translation engines. Training data can be mined as translation pairs from single content items that contain multiple languages; multiple content items in different languages that are related to the same or similar target; or multiple content items that are generated by the same author in different languages. Locating content items can include identifying potential sources of translation pairs that fall into these categories and applying filtering techniques to quickly gather those that are good candidates for being actual translation pairs. When actual translation pairs are located, they can be used to retrain a machine translation engine as in-domain for social media content items.

Abstract: Speech translation systems and methods for simultaneously translating speech between first and second speakers, wherein the first speaker speaks in a first language and the second speaker speaks in a second language that is different from the first language. The speech translation system may comprise a resegmentation unit that merge at least two partial hypotheses and resegments the merged partial hypotheses into a first-language translatable segment, wherein a segment boundary for the first-language translatable segment is determined based on sound from the second speaker.

Abstract: A speech translation system and methods for cross-lingual communication that enable users to improve and customize content and usage of the system and easily. The methods include, in response to receiving an utterance including a first term associated with a field, translating the utterance into a second language. In response to receiving an indication to add the first term associated with the field to a first recognition lexicon, adding the first term associated with the field and the determined translation to a first machine translation module and to a shared database for a community associated with the field of the first term associated with the field, wherein the first term associated with the field added to the shared database is accessible by the community.

Abstract: Computer-based systems and methods are disclosed for translation of a multi-media presentation (e.g., a lecture) along with the accompanying presentation materials. Translation and delivery of text-based presentation materials to a listener is annotated and aligned with audio, so that the listener can follow both the audio and the presentation material. The content of the presentation materials can be used to improve presentation translation, as it extracts supportive material from the presentation materials as they relate to the speech.

Abstract: Speech translation systems and methods for simultaneously translating speech between first and second speakers, wherein the first speaker speaks in a first language and the second speaker speaks in a second language that is different from the first language. The speech translation system may comprise a resegmentation unit that merge at least two partial hypotheses and resegments the merged partial hypotheses into a first-language translatable segment, wherein a segment boundary for the first-language translatable segment is determined based on sound from the second speaker.

Abstract: Computer-implemented systems and methods for extracting information during a human-to-human mono-lingual or multi-lingual dialog between two speakers are disclosed. Information from either the recognized speech (or the translation thereof) by the second speaker and/or the recognized speech by the first speaker (or the translation thereof) is extracted. The extracted information is then entered into an electronic form stored in a data store.

Abstract: Speech translation systems and methods for simultaneously translating speech between first and second speakers, wherein the first speaker speaks in a first language and the second speaker speaks in a second language that is different from the first language. The speech translation system may comprise a resegmentation unit that merge at least two partial hypotheses and resegments the merged partial hypotheses into a first-language translatable segment, wherein a segment boundary for the first-language translatable segment is determined based on sound from the second speaker.

Abstract: A hybrid speech translation system whereby a wireless-enabled client computing device can, in an offline mode, translate input speech utterances from one language to another locally, and also, in an online mode when there is wireless network connectivity, have a remote computer perform the translation and transmit it back to the client computing device via the wireless network for audible outputting by client computing device. The user of the client computing device can transition between modes or the transition can be automatic based on user preferences or settings. The back-end speech translation server system can adapt the various recognition and translation models used by the client computing device in the offline mode based on analysis of user data over time, to thereby configure the client computing device with scaled-down, yet more efficient and faster, models than the back-end speech translation server system, while still be adapted for the user's domain.

Abstract: Computer-implemented systems and methods for extracting information during a human-to-human mono-lingual or multi-lingual dialog between two speakers are disclosed. Information from either the recognized speech (or the translation thereof) by the second speaker and/or the recognized speech by the first speaker (or the translation thereof) is extracted. The extracted information is then entered into an electronic form stored in a data store.