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 contact center analysis system can receive various types of communications from customers, such as audio from telephone calls, voicemails, or video conferences; text from speech-to-text translations, emails, live chat transcripts, text messages, and the like; and other media or multimedia. The system can segment the communication data using temporal, lexical, semantic, syntactic, prosodic, user, and/or other features of the segments. The system can cluster the segments according to one or more similarity measures of the segments. The system can use the clusters to train a machine learning classifier to identify one or more of the clusters as waypoints (e.g., portions of the communications of particular relevance to a user training the classifier). The system can automatically classify new communications using the classifier and facilitate various analyses of the communications using the waypoints.

Systems and methods for cognitive training using voice command are described. One aspect includes a device to repeatedly present visual stimuli to a user that require performance of a task. A microphone may be positioned to provide audio input from the user to the device, with the audio input from the user providing input required to measure task performance. A processing system may perform real-time analysis of measured task performance.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, relating to synthetic call status updates. In some implementations, a method includes determining, by a task manager module, that a triggering event has occurred to provide a current status of a user call request. The method may then determine, by the task manager module, the current status of the user call request. A representation of the current status of the user call request is generated. Then, the generated representation of the current status of the user call request is provided to the user.

Techniques for ensuring content output to a user conforms to a quality of the user's speech, even when a speechlet or skill ignores the speech's quality, are described. When a system receives speech, the system determines an indicator of the speech's quality (e.g., whispered, shouted, fast, slow, etc.) and persists the indicator in memory. When the system receives output content from a speechlet or skill, the system checks whether the output content is in conformity with the speech quality indicator. If the content conforms to the speech quality indicator, the system may cause the content to be output to the user without further manipulation. But, if the content does not conform to the speech quality indicator, the system may manipulate the content to render it in conformity with the speech quality indicator and output the manipulated content to the user.

An electronic device and method are disclosed herein. The electronic device includes a microphone, a camera, an output device, a memory, and a processor. The processor implements the method, including receiving a voice input and/or capturing an image, and analyze the first voice input or the image to determine at least one of a user's intent, emotion, and situation based on predefined keywords and expressions, identifying a category based on the input, selecting first information based on the category, selecting and outputting a first query prompting confirmation of output of the first information, detect a first responsive input to the first query, and when a condition to output the first information is satisfied, output a second query, detecting a second input responsive to the second query, and selectively outputting the first information based on the second input.

Among other things, embodiments of the present disclosure relate to generating search queries based on audio processing. Other embodiments may be described and/or claimed.

An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions. These actions comprise: generating an annotated utterance tree of an utterance using a combination of rules-based and machine-learning (ML)-based components, wherein a structure of the annotated utterance tree represents a syntactic structure of the utterance, and wherein nodes of the annotated utterance tree include word vectors that represent semantic meanings of words of the utterance; and using the annotated utterance tree as a basis for intent/entity extraction of the utterance.

A system for monitoring and improving social agent interaction quality includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive, from a social agent, interaction data describing an interaction of the social agent with a user, and to perform an assessment of the interaction, using the interaction data, as one of successful or including a flaw. When the assessment indicates that the interaction includes the flaw, the processing hardware is further configured to execute the software code to identify an interaction strategy for correcting the flaw, and to deliver, to the social agent, one or both of the assessment and the interaction strategy to correct the flaw in the interaction.

Provided is a method for preventing an accident related to children or pets that may occur by a home appliance using artificial intelligence. According to the present disclosure, the method for preventing the accident comprises comparing a distance between the home appliance and a generation position of the voice signal and a reference distance when the generation position of the voice signal is outside of the home appliance. Then, the present disclosure enables switching a door of the home appliance to a lock state when a distance between the home appliance and the generation position of the voice signal is less than the reference distance. Thus, the present disclosure may enable controlling the home appliance to prevent children or pets from entering an inside of the home appliance.