Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

An autonomously motile device may be controlled by speech received by a user device. A first speech-processing system associated with the user device may determine that audio data includes a representation of a command; a second speech-processing system associated with the autonomously motile device may determine that the command should be executed by the autonomously motile device. A network connection is established between the user device and the autonomously motile device, and a device manager authorizes execution of the command.

An artificial intelligence device for providing a voice recognition service includes a microphone configured to receive a voice command, a memory configured to store an error analysis model for inferring an error cause of voice recognition, an output unit, and a processor configured to determine whether voice recognition of the voice command has failed based on the voice command and voice recognition surrounding information, acquire the error cause from the voice recognition surrounding information using the error analysis model, and output the acquired error cause through the output unit.

A system and method of responding to a vocal utterance may include capturing and converting the utterance to word(s) using a language processing method, such as natural language processing. The context of the utterance and of the system, which may include multimodal inputs, may be used to determine the meaning and intent of the words.

Conversational image editing and enhancement techniques are described. For example, an indication of a digital image is received from a user. Aesthetic attribute scores for multiple aesthetic attributes of the image are generated. A computing device then conducts a natural language conversation with the user to edit the digital image. The computing device receives inputs from the user to refine the digital image as the natural language conversation progresses. The computing device generates natural language suggestions to edit the digital image based on the aesthetic attribute scores as part of the natural language conversation. The computing device provides feedback to the user that includes edits to the digital image based on the series of inputs. The computing device also includes as feedback natural language outputs indicating options for additional edits to the digital image based on the series of inputs and the previous edits to the digital image.

The present application relates to a human-machine interaction method and device, a computer apparatus, and a storage medium. The method comprises: measuring the current output volume, and if the output volume is less than a first preset threshold, enabling a voice recognition function; acquiring a user's voice message, and measuring the size of the user's voice volume and responding to a user's voice operation; and if the user's voice volume is greater than a second preset threshold, turning down the output volume, and returning to the step of measuring the current output volume. In the entire process, the voice recognition function is controlled to be enabled by means of the output volume of an apparatus itself, thereby accurately responding to the user's voice operation, and if the user's voice is greater than a specified value, turning down the output volume, so that a user's subsequent voice message can be highlighted and accurately acquired so as to bring convenience to a user's operation and implement good human-machine interaction.

Disclosed is a server for supporting a communication environment between different electronic devices. The server includes a communication circuit, a memory, and a processor. The processor is electrically connected to the communication circuit and the memory. The processor is configured to receive a first voice signal transmitted from a second electronic device to a first electronic device through the communication circuit. The Processor is also configured to allow the first electronic device to transmit network connection information for connecting with the server to the second electronic device based on whether the first voice signal corresponds to a second voice signal stored in the memory.

Disclosed is an artificial intelligence server. The artificial intelligence server includes a communicator in communication with at least one electronic device and a processor for receiving input data from a specific electronic device, applying personalized information corresponding to the specific electronic device to a recognition model, inputting the input data into the recognition model to which the personalized information is applied to obtain a final result value, and transmitting the final result value to the specific electronic device.

Aspects of the subject disclosure may include, for example, a method in which a processing system loads into cloud storage items of information for potential delivery to a customer, and receives a customer inquiry that includes text, audio, and/or image data. The system generates input for a natural language processing (NLP) model. The input corresponds to the customer inquiry, and the generating comprises converting a format of the customer inquiry to a text format, using a cloud service. The system also analyzes the input using the NLP model, to predict a set of items of information to be included in a customer solution; the items of information are associated with identifying codes. The system also performs searching, using the identifying codes, the cloud storage to retrieve the predicted items of information, resulting in a customer solution for delivery to equipment of the customer. Other embodiments are disclosed.

Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.