An example embodiment may involve a software application executable on computing devices of a remote network management platform containing a computational instance associated with a managed network. A text string may be received, and characters of the string may be categorized among a plurality of symbol script families. A respective likelihood of the string corresponding to each family may be determined, and a respective probability of the string being in each language of each given family may also be determined. The respective probabilities for the languages of each given family may be weighted by the likelihoods of the given family, and then weighted sums of the probabilities for each language may be computed. The maximum of the weighted sums may correspond to the language of the text string. The respective probabilities may be determined according to hybrid N-gram and word language models for each family.

Systems and methods are provided for language-based service hailing. Such system may comprise one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the computing system to obtain a plurality of speech samples, each speech sample comprising one or more words spoken in a language, train a neural network model with the speech samples to obtain a trained model for determining languages of speeches, obtain a voice input, identify at least one language corresponding to the voice based at least on applying the trained model to the voice input, and communicate a message in the identified language.

A computer-implemented conferencing method is disclosed. A conference session between a user and one or more other conference participants is initiated via a computer conference application. An attribute-specific pronunciation of the user's name is determined via one or more attribute-specific-pronunciation machine-learning models previously trained based at least on one or more attributes of the one or more other conference participants. The attribute-specific pronunciation of the user's name is compared to a preferred pronunciation of the user's name via computer-pronunciation-comparison logic. Based on the attribute-specific pronunciation of the user's name being inconsistent with the preferred pronunciation of the user's name, a pronunciation learning protocol is automatically executed to convey, via the computer conference application, the preferred pronunciation of the user's name to the one or more other conference participants.

A method for determining an official transcription and speaker language from a plurality of transcripts of text in different languages. The method includes receiving a preselection of a plurality of different languages in which a first speaker can speak during a session of a cloud-based meeting; receiving, from a microphone, first audio content which originated from the first speaker; transcribing the first audio content of the first speaker into text of all the plurality of languages in which the first speaker can speak to generate a first plurality of text transcripts; identifying a first untranslated speech bubble as making the most sense among the first plurality of transcripts for the first speaker; and adding the first untranslated speech bubble to a master transcript for the first speaker.

A method for determining an official transcription and speaker language from a plurality of transcripts of text in different languages. The method includes receiving a preselection of a plurality of different languages in which a first speaker can speak during a session of a cloud-based meeting; receiving, from a microphone, first audio content which originated from the first speaker; transcribing the first audio content of the first speaker into text of all the plurality of languages in which the first speaker can speak to generate a first plurality of text transcripts; identifying a first untranslated speech bubble as making the most sense among the first plurality of transcripts for the first speaker; and adding the first untranslated speech bubble to a master transcript for the first speaker.

A computer assesses language attributes of web application display text elements. The computer receives access to a selected web application. The computer parses hypertext markup language content of the web application and generating a parse tree representing the content. The computer identifies, using the parse tree, display text elements within the content and determining associated element selector queries that identify respective display text elements within the parse tree. The computer processes a set of display text elements, using a plurality of Natural Language Processing classifier models, each of the classifier models generates a relevant language prediction for the processed display text element. The computer collects, for each text element, groups of classifiers associated with substantially-similar predictions and indexed by relevant text element selector. The computer determines a target language match condition for each group. The computer initiates a corresponding at least one corrective action associated with the match condition.

A computer assesses language attributes of web application display text elements. The computer receives access to a selected web application. The computer parses hypertext markup language content of the web application and generating a parse tree representing the content. The computer identifies, using the parse tree, display text elements within the content and determining associated element selector queries that identify respective display text elements within the parse tree. The computer processes a set of display text elements, using a plurality of Natural Language Processing classifier models, each of the classifier models generates a relevant language prediction for the processed display text element. The computer collects, for each text element, groups of classifiers associated with substantially-similar predictions and indexed by relevant text element selector. The computer determines a target language match condition for each group. The computer initiates a corresponding at least one corrective action associated with the match condition.

The present disclosure relates to an audio information processing method, an apparatus, an electronic device and a computer-readable storage medium. The audio information processing method includes: determining whether an audio recording start condition is satisfied; collecting audio information associated with an electronic device in response to determining that the audio recording start condition is satisfied; performing word segmentation on text information corresponding to the audio information to obtain word-segmented text information; and displaying the word-segmented text information on a user interface of the electronic device.

The present disclosure relates to an audio information processing method, an apparatus, an electronic device and a computer-readable storage medium. The audio information processing method includes: determining whether an audio recording start condition is satisfied; collecting audio information associated with an electronic device in response to determining that the audio recording start condition is satisfied; performing word segmentation on text information corresponding to the audio information to obtain word-segmented text information; and displaying the word-segmented text information on a user interface of the electronic device.

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.