An employee of a large organization sends a human-readable document such as an email or text message to another employee of the organization to inform the other employee of a change in availability. A trained machine-learning model extracts, from the human-readable document, data used by a leave management system (LMS) to formalize and memorialize the leave request. For example, the employee name, manager name, date leave begins, date leave ends, reason for the leave request, or any suitable combination thereof may be determined by the machine-learning model based on the human-readable document. The extracted data is provided to the LMS and the leave request is created.

Sentiment classification can be implemented by an entity-level multimodal sentiment classification neural network. The neural network can include left, right, and target entity subnetworks. The neural network can further include an image network that generates representation data that is combined and weighted with data output by the left, right, and target entity subnetworks to output a sentiment classification for an entity included in a network post.

A centralized system may collect and aggregate assessments from multiple websites. An aggregate score may be calculated for the resource that cumulatively considers assessments from a plurality of different websites from which assessments are received from users. Text descriptions associated with each of the assessments may be provided to a machine-learning system that uses a trained model to assign identifiers to the assessments as they are received. These identifiers may include common words or text that are descriptive of different facets of user experiences related to receiving and using the resource. After selecting one or more identifiers, assessments associated with that identifier may be included or excluded from the display. Additionally, the overall aggregate score for the resource may be recalculated by removing components of that score that are based on assessments with identifiers that have been selected for exclusion.

Embodiments of the invention are directed to systems, methods, and computer program products for a scheme evaluation authentication system to evaluate user action and machine learned pattern recognition for misappropriation activity prevention. In this way, bot and token deployment is utilized for identification of user actions to generate a user idiosyncrasy database. Using the database, the system creates a real-time scheme evaluation authentication for user authentication upon user attempted authentications. As such, a user may by authenticate irrespective of user inputs, but instead based on a match of user actions at the authentication location in real-time compared to the idiosyncrasy database.

Embodiments of the invention are directed to systems, methods, and computer program products for a scheme evaluation authentication system to evaluate user action and machine learned pattern recognition for misappropriation activity prevention. In this way, bot and token deployment is utilized for identification of user actions to generate a user idiosyncrasy database. Using the database, the system creates a real-time scheme evaluation authentication for user authentication upon user attempted authentications. As such, a user may by authenticate irrespective of user inputs, but instead based on a match of user actions at the authentication location in real-time compared to the idiosyncrasy database.

Intelligent reframing techniques are described in which content (e.g., a movie) can be generated in a different aspect ratio than previously provided. These techniques include obtaining various video frames having a first aspect ratio. Various objects can be identified within the frames. An object having the highest degree of importance in a frame can be selected and a focal point can be calculated based at least in part on that object. A modified version of the content can be generated in a second aspect ratio that is different from the first aspect ratio. The modified version can be generated using the focal point calculated based on the object having the greatest degree of importance. Using these techniques, the content can be provided in a different aspect ratio while ensuring that the most important features of the frame still appear in the new version of the content.

An embodiment includes converting an original audio signal to an original text string, the original audio signal being from a recording of the original text string spoken by a specific person in a source language. The embodiment generates a translated text string by translating the original text string from the source language to a target language, including translation of a word from the source language to a target language. The embodiment assembles a standard phoneme sequence from a set of standard phonemes, where the standard phoneme sequence includes a standard pronunciation of the translated word. The embodiment also associates a custom phoneme with a standard phoneme of the standard phoneme sequence, where the custom phoneme includes the specific person's pronunciation of a sound in the translated word. The embodiment synthesizes the translated text string to a translated audio signal including the translated word pronounced using the custom phoneme.

An embodiment includes converting an original audio signal to an original text string, the original audio signal being from a recording of the original text string spoken by a specific person in a source language. The embodiment generates a translated text string by translating the original text string from the source language to a target language, including translation of a word from the source language to a target language. The embodiment assembles a standard phoneme sequence from a set of standard phonemes, where the standard phoneme sequence includes a standard pronunciation of the translated word. The embodiment also associates a custom phoneme with a standard phoneme of the standard phoneme sequence, where the custom phoneme includes the specific person's pronunciation of a sound in the translated word. The embodiment synthesizes the translated text string to a translated audio signal including the translated word pronounced using the custom phoneme.

Embodiments are directed to organizing conversation information. Two or more machine learning (ML) models and a plurality of sentences provided from a conversation may be employed to generate insight scores for each sentence such that each insight score correlates to a probability that its sentence includes one or more of an action or a question. In response to one or more sentences having insight scores that exceed a threshold value an information score and a definiteness score may be determined for the one or more sentences. And one or more insights associated with the conversation may be generated based on the one or more sentences. A report may be generated that associates the one or more insights with one or more portions of the conversation that include the one or more sentences that are associated with the insights.

Embodiments are directed to organizing conversation information. Two or more machine learning (ML) models and a plurality of sentences provided from a conversation may be employed to generate insight scores for each sentence such that each insight score correlates to a probability that its sentence includes one or more of an action or a question. In response to one or more sentences having insight scores that exceed a threshold value an information score and a definiteness score may be determined for the one or more sentences. And one or more insights associated with the conversation may be generated based on the one or more sentences. A report may be generated that associates the one or more insights with one or more portions of the conversation that include the one or more sentences that are associated with the insights.