Abstract: A computer-implemented input-method editor process includes receiving a request from a user for an application-independent input method editor having written and spoken input capabilities, identifying that the user is about to provide spoken input to the application-independent input method editor, and receiving a spoken input from the user. The spoken input corresponds to input to an application and is converted to text that represents the spoken input. The text is provided as input to the application.

Abstract: A computer-implemented input-method editor process includes receiving a request from a user for an application-independent input method editor having written and spoken input capabilities, identifying that the user is about to provide spoken input to the application-independent input method editor, and receiving a spoken input from the user. The spoken input corresponds to input to an application and is converted to text that represents the spoken input. The text is provided as input to the application.

Abstract: A computer-implemented input-method editor process includes receiving a request from a user for an application-independent input method editor having written and spoken input capabilities, identifying that the user is about to provide spoken input to the application-independent input method editor, and receiving a spoken input from the user. The spoken input corresponds to input to an application and is converted to text that represents the spoken input. The text is provided as input to the application.

Abstract: A computer-implemented input-method editor process includes receiving a request from a user for an application-independent input method editor having written and spoken input capabilities, identifying that the user is about to provide spoken input to the application-independent input method editor, and receiving a spoken input from the user. The spoken input corresponds to input to an application and is converted to text that represents the spoken input. The text is provided as input to the application.

Abstract: A computer-implemented input-method editor process includes receiving a request from a user for an application-independent input method editor having written and spoken input capabilities, identifying that the user is about to provide spoken input to the application-independent input method editor, and receiving a spoken input from the user. The spoken input corresponds to input to an application and is converted to text that represents the spoken input. The text is provided as input to the application.

Abstract: A method for generating a speech recognition model includes accessing a baseline speech recognition model, obtaining information related to recent language usage from search queries, and modifying the speech recognition model to revise probabilities of a portion of a sound occurrence based on the information. The portion of a sound may include a word. Also, a method for generating a speech recognition model, includes receiving at a search engine from a remote device an audio recording and a transcript that substantially represents at least a portion of the audio recording, synchronizing the transcript with the audio recording, extracting one or more letters from the transcript and extracting the associated pronunciation of the one or more letters from the audio recording, and generating a dictionary entry in a pronunciation dictionary.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for speech recognition. One of the methods includes receiving a base language model for speech recognition including a first word sequence having a base probability value; receiving a voice search query associated with a query context; determining that a customized language model is to be used when the query context satisfies one or more criteria associated with the customized language model; obtaining the customized language model, the customized language model including the first word sequence having an adjusted probability value being the base probability value adjusted according to the query context; and converting the voice search query to a text search query based on one or more probabilities, each of the probabilities corresponding to a word sequence in a group of one or more word sequences, the group including the first word sequence having the adjusted probability value.

Abstract: A system and method implemented as computer-based, computer-executable instructions employing a computation engine with digital storage provide a virtual environment with personalized user profiles specifically adapted to cognitive abilities of the user to train the user for specified tasks, herein called microtasks. The set of microtasks include achievement criteria. The virtual environment is controlled through an engine that automatically adapts the set of user-profile-specific microtasks to achieve a set of learning goals. The engine may calculate and/or measure a set of qualities associated with one or more sub-profiles associated with a given user based on game-type performance by that user either in isolation or among a group of users. Sub-profiles may include, but are not limited to, one or more of the following: a personality profile, a neural link profile, and/or a motivation profile.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for speech recognition. One of the methods includes receiving a base language model for speech recognition including a first word sequence having a base probability value; receiving a voice search query associated with a query context; determining that a customized language model is to be used when the query context satisfies one or more criteria associated with the customized language model; obtaining the customized language model, the customized language model including the first word sequence having an adjusted probability value being the base probability value adjusted according to the query context; and converting the voice search query to a text search query based on one or more probabilities, each of the probabilities corresponding to a word sequence in a group of one or more word sequences, the group including the first word sequence having the adjusted probability value.

Abstract: A computer-implemented input-method editor process includes receiving a request from a user for an application-independent input method editor having written and spoken input capabilities, identifying that the user is about to provide spoken input to the application-independent input method editor, and receiving a spoken input from the user. The spoken input corresponds to input to an application and is converted to text that represents the spoken input. The text is provided as input to the application.

Abstract: A computer-implemented input-method editor process includes receiving a request from a user for an application-independent input method editor having written and spoken input capabilities, identifying that the user is about to provide spoken input to the application-independent input method editor, and receiving a spoken input from the user. The spoken input corresponds to input to an application and is converted to text that represents the spoken input. The text is provided as input to the application.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for speech recognition. One of the methods includes receiving a base language model for speech recognition including a first word sequence having a base probability value; receiving a voice search query associated with a query context; determining that a customized language model is to be used when the query context satisfies one or more criteria associated with the customized language model; obtaining the customized language model, the customized language model including the first word sequence having an adjusted probability value being the base probability value adjusted according to the query context; and converting the voice search query to a text search query based on one or more probabilities, each of the probabilities corresponding to a word sequence in a group of one or more word sequences, the group including the first word sequence having the adjusted probability value.

Abstract: A method for generating a speech recognition model includes accessing a baseline speech recognition model, obtaining information related to recent language usage from search queries, and modifying the speech recognition model to revise probabilities of a portion of a sound occurrence based on the information. The portion of a sound may include a word. Also, a method for generating a speech recognition model, includes receiving at a search engine from a remote device an audio recording and a transcript that substantially represents at least a portion of the audio recording, synchronizing the transcript with the audio recording, extracting one or more letters from the transcript and extracting the associated pronunciation of the one or more letters from the audio recording, and generating a dictionary entry in a pronunciation dictionary.

Abstract: A system and method implemented as computer-based, computer-executable instructions employing a computation engine with digital storage provide a virtual environment with personalized user profiles specifically adapted to cognitive abilities of the user to train the user for specified tasks, herein called microtasks. The set of microtasks include achievement criteria. The virtual environment is controlled through an engine that automatically adapts the set of user-profile-specific microtasks to achieve a set of learning goals. The engine may calculate and/or measure a set of qualities associated with one or more sub-profiles associated with a given user based on game-type performance by that user either in isolation or among a group of users. Sub-profiles may include, but are not limited to, one or more of the following: a personality profile, a neural link profile, and/or a motivation profile.

Abstract: A method for generating a speech recognition model includes accessing a baseline speech recognition model, obtaining information related to recent language usage from search queries, and modifying the speech recognition model to revise probabilities of a portion of a sound occurrence based on the information. The portion of a sound may include a word. Also, a method for generating a speech recognition model, includes receiving at a search engine from a remote device an audio recording and a transcript that substantially represents at least a portion of the audio recording, synchronizing the transcript with the audio recording, extracting one or more letters from the transcript and extracting the associated pronunciation of the one or more letters from the audio recording, and generating a dictionary entry in a pronunciation dictionary.

Abstract: A system and method implemented as computer-based, computer-executable instructions employing a computation engine with digital storage provide a virtual environment with personalized user profiles specifically adapted to cognitive abilities of the user to train the user for specified tasks, herein called microtasks. The set of microtasks include achievement criteria. The virtual environment is controlled through an engine that automatically adapts the set of user-profile-specific microtasks to achieve a set of learning goals. The engine may calculate and/or measure a set of qualities associated with one or more sub-profiles associated with a given user based on game-type performance by that user either in isolation or among a group of users. Sub-profiles may include, but are not limited to, one or more of the following: a personality profile, a neural link profile, and/or a motivation profile.

Abstract: A system and method implemented as computer-based, computer-executable instructions employing a computation engine with digital storage provide a virtual environment with personalized user profiles specifically adapted to cognitive abilities of the user to train the user for specified tasks, herein called microtasks. The set of microtasks include achievement criteria. The virtual environment is controlled through an engine that automatically adapts the set of user-profile-specific microtasks to achieve a set of learning goals. The engine may calculate and/or measure a set of qualities associated with one or more sub-profiles associated with a given user based on game-type performance by that user either in isolation or among a group of users. Sub-profiles may include, but are not limited to, one or more of the following: a personality profile, a neural link profile, and/or a motivation profile.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for speech recognition. One of the methods includes receiving a base language model for speech recognition including a first word sequence having a base probability value; receiving a voice search query associated with a query context; determining that a customized language model is to be used when the query context satisfies one or more criteria associated with the customized language model; obtaining the customized language model, the customized language model including the first word sequence having an adjusted probability value being the base probability value adjusted according to the query context; and converting the voice search query to a text search query based on one or more probabilities, each of the probabilities corresponding to a word sequence in a group of one or more word sequences, the group including the first word sequence having the adjusted probability value.

Abstract: A system and method implemented as computer-based, computer-executable instructions employing a computation engine with digital storage provide a virtual environment with personalized user profiles specifically adapted to cognitive abilities of the user to train the user for specified tasks, herein called microtasks. The set of microtasks include achievement criteria. The virtual environment is controlled through an engine that automatically adapts the set of user-profile-specific microtasks to achieve a set of learning goals. The engine may calculate and/or measure a set of qualities associated with one or more sub-profiles associated with a given user based on game-type performance by that user either in isolation or among a group of users. Sub-profiles may include, but are not limited to, one or more of the following: a personality profile, a neural link profile, and/or a motivation profile.

Abstract: Methods, computer program products and systems are described for converting speech to text. Sound information is received at a computer server system from an electronic device, where the sound information is from a user of the electronic device. A context identifier indicates a context within which the user provided the sound information. The context identifier is used to select, from among multiple language models, a language model appropriate for the context. Speech in the sound information is converted to text using the selected language model. The text is provided for use by the electronic device.