Abstract: Systems and methods may be used to provide transcription and translation services. A method may include initializing a plurality of user devices with respective language output selections in a translation group by receiving a shared identifier from the plurality of user devices and transcribing the audio stream to transcribed text. The method may include translating the transcribed text to one or more of the respective language output selections when an original language of the transcribed text differs from the one or more of the respective language output selections. The method may include sending, a user device in the translation group, the transcribed text including translated text in a language corresponding to the respective language output selection for the user device. In an example, the method may include customizing the transcription or the translation, such as to a particular topic, location, user, or the like.

Abstract: Systems and methods may be used to provide transcription and translation services. A method may include initializing a plurality of user devices with respective language output selections in a translation group by receiving a shared identifier from the plurality of user devices and transcribing the audio stream to transcribed text. The method may include translating the transcribed text to one or more of the respective language output selections when an original language of the transcribed text differs from the one or more of the respective language output selections. The method may include sending, a user device in the translation group, the transcribed text including translated text in a language corresponding to the respective language output selection for the user device. In an example, the method may include customizing the transcription or the translation, such as to a particular topic, location, user, or the like.

Abstract: Systems and methods may be used to provide transcription and translation services. A method may include initializing a plurality of user devices with respective language output selections in a translation group by receiving a shared identifier from the plurality of user devices and transcribing the audio stream to transcribed text. The method may include translating the transcribed text to one or more of the respective language output selections when an original language of the transcribed text differs from the one or more of the respective language output selections. The method may include sending, a user device in the translation group, the transcribed text including translated text in a language corresponding to the respective language output selection for the user device. In an example, the method may include customizing the transcription or the translation, such as to a particular topic, location, user, or the like.

Abstract: Systems and methods may be used to provide transcription and translation services. A method may include initializing a plurality of user devices with respective language output selections in a translation group by receiving a shared identifier from the plurality of user devices and transcribing the audio stream to transcribed text. The method may include translating the transcribed text to one or more of the respective language output selections when an original language of the transcribed text differs from the one or more of the respective language output selections. The method may include sending, a user device in the translation group, the transcribed text including translated text in a language corresponding to the respective language output selection for the user device. In an example, the method may include customizing the transcription or the translation, such as to a particular topic, location, user, or the like.

Abstract: Systems and methods may be used to provide transcription and translation services. A method may include initializing a plurality of user devices with respective language output selections in a translation group by receiving a shared identifier from the plurality of user devices and transcribing the audio stream to transcribed text. The method may include translating the transcribed text to one or more of the respective language output selections when an original language of the transcribed text differs from the one or more of the respective language output selections. The method may include sending, a user device in the translation group, the transcribed text including translated text in a language corresponding to the respective language output selection for the user device. In an example, the method may include customizing the transcription or the translation, such as to a particular topic, location, user, or the like.

Abstract: Technologies are described herein for cross-language speech recognition and translation. An example method of speech recognition and translation includes receiving an input utterance in a first language, the input utterance having at least one name of a named entity included therein and being pronounced in a second language, utilizing a customized language model to process at least a portion of the input utterance, and identifying the at least one name of the named entity from the input utterance utilizing a phonetic representation of the at least one name of the named entity. The phonetic representation has a pronunciation of the at least one name in the second language.

Abstract: Systems and methods may be used to provide transcription and translation services. A method may include initializing a plurality of user devices with respective language output selections in a translation group by receiving a shared identifier from the plurality of user devices and transcribing the audio stream to transcribed text. The method may include translating the transcribed text to one or more of the respective language output selections when an original language of the transcribed text differs from the one or more of the respective language output selections. The method may include sending, a user device in the translation group, the transcribed text including translated text in a language corresponding to the respective language output selection for the user device. In an example, the method may include customizing the transcription or the translation, such as to a particular topic, location, user, or the like.

Abstract: The assistive hearing device implementations described herein assist hearing impaired users of the device by using automated speech transcription to generate text representing speech received in audio signals which can then be read in a synthesized voice tailored to overcome a user's hearing deficiencies. A speech recognition engine recognizes speech in received audio and converts the speech of the received audio to text. Once the speech is converted to text, a text-to-speech engine can convert the text to synthesized speech that can be enhanced and output in a voice that compensates for the hearing loss profiles of a user of the assistive hearing device. By transcribing received speech into text the assistive hearing device implementations described herein eliminate background noise from the audio signal. By converting the transcribed text into a synthesized voice that is easier to understand to hearing impaired persons, their hearing deficiencies can be remedied.

Abstract: The disclosure pertains to a communication system for effecting a voice or video call between at least a source user speaking a source language and a target user speaking a target language. A translation procedure is performed on call audio of the call to generate an audio translation of the source user's speech in the target language for outputting to the target user. A notification is outputted to the target user to notify the target user of a change in the behavior of the translation procedure, the change relating to the generation of the translation.

Abstract: The personal translator implementations described herein provide a speech translation device that pairs with a computing device to translate in-person conversations. The speech translation device can be wearable. In one implementation the personal translator comprises a speech translation device with at least one microphone that captures input signals representing nearby speech of a first user/wearer of the device and at least one other nearby person in a conversation in two languages; a wireless communication unit that sends the captured input signals representing speech to a nearby computing device, and receives for each language in the conversation, language translations from the computing device; and at least one loudspeaker that outputs the language translations to the first user/wearer and at least one other nearby person. The language translations in text form can be displayed on a display at the same time the language translations are output to the loudspeaker(s).

Abstract: Technologies are described herein for cross-language speech recognition and translation. An example method of speech recognition and translation includes receiving an input utterance in a first language, the input utterance having at least one name of a named entity included therein and being pronounced in a second language, utilizing a customized language model to process at least a portion of the input utterance, and identifying the at least one name of the named entity from the input utterance utilizing a phonetic representation of the at least one name of the named entity. The phonetic representation has a pronunciation of the at least one name in the second language.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifested as a system that can include a cache module and at least one processing device configured to execute the cache module. The cache module can be configured to store data items in slots of a cache structure, receive a request for an individual data item that maps to an individual slot of the cache structure, and, when the individual slot of the cache structure is not available, return without further processing the request. For example, the request can be received from a calling application or thread that can proceed without blocking irrespective of whether the request is fulfilled by the cache module.

Abstract: Call audio of a call between a source user speaking a source language and a target user speaking a target language is received from a remote source user device of a source user via a communication network of a communication system, the call audio comprising speech of the source user in the source language. An automatic speech recognition procedure is performed on the call audio. A translation of the source user's speech is generated in the target language using the results of the speech recognition procedure. A translated synthetic speech audio version of the source user's speech is mixed with the source user's call audio and/or with translated audio of the target user's speech in the source language. The mixed audio signal is transmitted to a remote target user device of the target user via the communication network for outputting to at least the target user during the call.

Abstract: The disclosure pertains to a communication system for effecting a voice or video call between at least a source user speaking a source language and a target user speaking a target language. A translation procedure is performed on call audio of the call to generate an audio translation of the source user's speech in the target language for outputting to the target user. A notification is outputted to the target user to notify the target user of a change in the behaviour of the translation procedure, the change relating to the generation of the translation.

Abstract: A method of translation includes uploading a source text portion to a back end processor. The back end processor identifies a subset of translation knowledge associated with the source text portion. The back end processor downloads the subset to a front end processor. A translation engine runs on the front end processor. The translation engine generates a translation of the source text portion as a function of the subset.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifested as a system that can include a cache module and at least one processing device configured to execute the cache module. The cache module can be configured to store data items in slots of a cache structure, receive a request for an individual data item that maps to an individual slot of the cache structure, and, when the individual slot of the cache structure is not available, return without further processing the request. For example, the request can be received from a calling application or thread that can proceed without blocking irrespective of whether the request is fulfilled by the cache module.

Abstract: A computer-implemented machine translation system translates text from a first language to a second language. The system includes a plurality of mappings, each mapping indicative of associating a dependency structure of the first language with a dependency structure of the second language, wherein at least some of the mappings correspond to dependency structures of the first language having varying context with some common elements, and associated dependency structures of the second language to the dependency structures of the first language. A module receives input text in a first language and outputs output text in a second language based on accessing the plurality of mappings.

Abstract: A method of translation includes uploading a source text portion to a back end processor. The back end processor identifies a subset of translation knowledge associated with the source text portion. The back end processor downloads the subset to a front end processor. A translation engine runs on the front end processor. The translation engine generates a translation of the source text portion as a function of the subset.

Abstract: A method of translation includes uploading a source text portion to a back end processor. The back end processor identifies a subset of translation knowledge associated with the source text portion. The back end processor downloads the subset to a front end processor. A translation engine runs on the front end processor. The translation engine generates a translation of the source text portion as a function of the subset.

Abstract: Many machine translation scenarios involve the generation of a language translation rule set based on parallel training corpuses (e.g., sentences in a first language and word-for-word translations into a second language.) However, the translation of a source corpus in a source language to a target corpus in a target language involves at least two aspects: selecting elements of the target language to match the elements of the source corpus, and ordering the target elements according to the semantic organization of the source corpus and the grammatic rules of the target language. The breadth of generalization of the translation rules derived from the training may be improved, while retaining contextual information, by formulating language order templates that specify orderings of small sets of target elements according to target element types.