In some implementations, a computing device can present a multi-language grouping of topics. For example, the computing device can determine a primary and secondary language for a user of the computing device. The computing device can request configuration that includes a tag language mapping that can be used to translate topic tags corresponding to the secondary language to topic tags corresponding to the primary language. When the computing device receives tagged content items associated with the secondary language, the computing device can translate the secondary language tags corresponding to the tagged content items into semantically equivalent topic tags in the primary language. The computing device can then group primary language content items and secondary language content items into multi-language groupings based on the topics corresponding to the translated content item tags. The computing device can then present the multi-language topic groupings of content items.

Various embodiments provide media based on a detected language being spoken. In one embodiment, the system electronically detects which language of a plurality of languages is being spoken by a user, such during a conversation or while giving a voice command to the television. Based on which language of a plurality of languages is being spoken by the user, the system electronically presents media to the user that is in the detected language. For example, the media may be television channels and/or programs that are in the detected language and/or a program guide, such as a pop-up menu, including such media that are in the detected language.

Various embodiments provide media based on a detected language being spoken. In one embodiment, the system electronically detects which language of a plurality of languages is being spoken by a user, such during a conversation or while giving a voice command to the television. Based on which language of a plurality of languages is being spoken by the user, the system electronically presents media to the user that is in the detected language. For example, the media may be television channels and/or programs that are in the detected language and/or a program guide, such as a pop-up menu, including such media that are in the detected language.

The present disclosure relates to a method of displaying two sets of characters, the method being implemented by a computing device, said method comprising: receiving, by the computing device, a first set of characters; receiving, by the computing device, a second set of characters; modifying, by the computing device, an appearance of one or more of the second set of characters to receive, without overlap, one or more of the first set of characters; and displaying, on a device screen, the one or more of the first set of characters and the modified second set of characters, wherein the one or more of the first set of characters are embedded in the modified second set of characters.

The present disclosure relates to a method of displaying two sets of characters, the method being implemented by a computing device, said method comprising: receiving, by the computing device, a first set of characters; receiving, by the computing device, a second set of characters; modifying, by the computing device, an appearance of one or more of the second set of characters to receive, without overlap, one or more of the first set of characters; and displaying, on a device screen, the one or more of the first set of characters and the modified second set of characters, wherein the one or more of the first set of characters are embedded in the modified second set of characters.

A system and method for voice-based control of sexual stimulation devices. In some configurations, the system and method involve receiving voice data, analyzing the voice data to detect spoken commands, and generating control signals based on the commands. In some configurations, the system and method involve receiving voice data, analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns. In some configurations, the analyses of the voice data are performed by machine learning algorithms which may be trained on associations between speech and non-speech vocalizations of a user while the user engages in one or more voice-based training tasks, associating speech and non-speech vocalizations with controls of the sexual stimulation device. In some configurations, machine learning algorithms are used to make the associations. In some configurations, data from other biometric sensors is included in the associations.

A system and method for voice-based control of sexual stimulation devices. In some configurations, the system and method involve receiving voice data, analyzing the voice data to detect spoken commands, and generating control signals based on the commands. In some configurations, the system and method involve receiving voice data, analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns. In some configurations, the analyses of the voice data are performed by machine learning algorithms which may be trained on associations between speech and non-speech vocalizations of a user while the user engages in one or more voice-based training tasks, associating speech and non-speech vocalizations with controls of the sexual stimulation device. In some configurations, machine learning algorithms are used to make the associations. In some configurations, data from other biometric sensors is included in the associations.

By analyzing a natural language content concurrently with a presentation of the natural language content, a first language of the natural language content is detected. By analyzing the natural language content concurrently with the presentation of the natural language content, it is detected that a subset of the natural language content is expressed in a second language, wherein the second language is different from the first language, wherein a set of known languages comprises the first language, wherein the set of known languages excludes the second language. From the subset of the natural language content, a modified subset expressed in the first language is generated. The modified subset is inserted into the natural language content.

By analyzing a natural language content concurrently with a presentation of the natural language content, a first language of the natural language content is detected. By analyzing the natural language content concurrently with the presentation of the natural language content, it is detected that a subset of the natural language content is expressed in a second language, wherein the second language is different from the first language, wherein a set of known languages comprises the first language, wherein the set of known languages excludes the second language. From the subset of the natural language content, a modified subset expressed in the first language is generated. The modified subset is inserted into the natural language content.

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.