A method, an electronic device, and computer readable medium is provided. The method includes identifying a frequency of each word that is present within a set of words. The method also includes deriving relatedness values for pairs of words. Each pair of words includes a first word and a second word in the set of words. Each relatedness value corresponds to a respective one of the pairs of words. Each relatedness value is based on the identified frequencies that the first word and the second word of the respective pair of words are present within the set of words. The method further includes generating a matrix representing the relatedness values. The method additionally includes generating a language model that represents relationships between the set of words included in the matrix.

A method, an electronic device, and computer readable medium is provided. The method includes identifying a frequency of each word that is present within a set of words. The method also includes deriving relatedness values for pairs of words. Each pair of words includes a first word and a second word in the set of words. Each relatedness value corresponds to a respective one of the pairs of words. Each relatedness value is based on the identified frequencies that the first word and the second word of the respective pair of words are present within the set of words. The method further includes generating a matrix representing the relatedness values. The method additionally includes generating a language model that represents relationships between the set of words included in the matrix.

Methods and systems for consent based information sharing. One system includes a server including an electronic processor configured to receive a first set of parsed communication data for a first talkgroup and a second set of parsed communication data for a second talkgroup. The electronic processor is configured to determine a topic of interest of the first talkgroup. The electronic processor is configured to identify relevant communication data from the second set of parsed communication data, where the relevant communication data is relevant to the topic of interest. The electronic processor is configured to determine whether the relevant communication data is shareable. The electronic processor is configured to, in response to determining that the relevant communication data is not shareable, request consent from the first talkgroup and the second talkgroup to share the relevant communication data, and, in response to receiving consent, enable sharing of the relevant communication data.

Methods and systems for consent based information sharing. One system includes a server including an electronic processor configured to receive a first set of parsed communication data for a first talkgroup and a second set of parsed communication data for a second talkgroup. The electronic processor is configured to determine a topic of interest of the first talkgroup. The electronic processor is configured to identify relevant communication data from the second set of parsed communication data, where the relevant communication data is relevant to the topic of interest. The electronic processor is configured to determine whether the relevant communication data is shareable. The electronic processor is configured to, in response to determining that the relevant communication data is not shareable, request consent from the first talkgroup and the second talkgroup to share the relevant communication data, and, in response to receiving consent, enable sharing of the relevant communication data.

Novel solutions for speech recognition provide contextual spelling correction (CSC) for automatic speech recognition (ASR). Disclosed examples include receiving an audio stream; performing an ASR process on the audio stream to produce an ASR hypothesis; receiving a context list; and, based on at least the ASR hypothesis and the context list, performing spelling correction to produce an output text sequence. A contextual spelling correction (CSC) model is used on top of an ASR model, precluding the need for changing the original ASR model. This permits run-time user customization based on contextual data, even for large-size context lists. Some examples include filtering ASR hypotheses for the audio stream and, based on at least the ASR hypotheses filtering, determining whether to trigger spelling correction for the ASR hypothesis. Some examples include generating text to speech (TTS) audio using preprocessed transcriptions with context phrases to train the CSC model.

Novel solutions for speech recognition provide contextual spelling correction (CSC) for automatic speech recognition (ASR). Disclosed examples include receiving an audio stream; performing an ASR process on the audio stream to produce an ASR hypothesis; receiving a context list; and, based on at least the ASR hypothesis and the context list, performing spelling correction to produce an output text sequence. A contextual spelling correction (CSC) model is used on top of an ASR model, precluding the need for changing the original ASR model. This permits run-time user customization based on contextual data, even for large-size context lists. Some examples include filtering ASR hypotheses for the audio stream and, based on at least the ASR hypotheses filtering, determining whether to trigger spelling correction for the ASR hypothesis. Some examples include generating text to speech (TTS) audio using preprocessed transcriptions with context phrases to train the CSC model.

The subject matter of this specification can be embodied in, among other things, a method that includes receiving geographical information derived from a non-verbal user action associated with a first computing device. The non-verbal user action implies an interest of a user in a geographic location. The method also includes identifying a grammar associated with the geographic location using the derived geographical information and outputting a grammar indicator for use in selecting the identified grammar for voice recognition processing of vocal input from the user.

The subject matter of this specification can be embodied in, among other things, a method that includes receiving geographical information derived from a non-verbal user action associated with a first computing device. The non-verbal user action implies an interest of a user in a geographic location. The method also includes identifying a grammar associated with the geographic location using the derived geographical information and outputting a grammar indicator for use in selecting the identified grammar for voice recognition processing of vocal input from the user.

Implementations relate to determining a secondary language proficiency measure, for a user in a secondary language (i.e., a language other than a primary language specified for the user), where determining the secondary language proficiency measure is based on past interactions of the user that are related to the secondary language. Those implementations further relate to utilizing the determined secondary language proficiency measure to increase efficiency of user interaction(s), such as interaction(s) with a language learning application and/or an automated assistant. Some of those implementations utilize the secondary language proficiency measure in automatically setting value(s), biasing automatic speech recognition, and/or determining how to render natural language output.

Implementations relate to determining a secondary language proficiency measure, for a user in a secondary language (i.e., a language other than a primary language specified for the user), where determining the secondary language proficiency measure is based on past interactions of the user that are related to the secondary language. Those implementations further relate to utilizing the determined secondary language proficiency measure to increase efficiency of user interaction(s), such as interaction(s) with a language learning application and/or an automated assistant. Some of those implementations utilize the secondary language proficiency measure in automatically setting value(s), biasing automatic speech recognition, and/or determining how to render natural language output.