The present disclosure provides a method for a human-computer interaction, an apparatus for a human-computer interaction, a device, and a storage medium, and the present disclosure relates to the field of artificial intelligence, such as deep learning and voice. A specific implementation includes: acquiring a voice command; performing voice recognition on the voice command to determine a corresponding voice text; sending, in response to satisfying a preset information sending condition, the voice text to a cloud; receiving a resource for the voice command returned from the cloud; and responding to the voice command based on the resource.

Disclosed herein are example techniques for voice detection by multiple NMDs. An example implementation may involve one or more servers receiving, via a network interface, data representing multiple audio recordings of a voice input spoken by a given user, each audio recording recorded by a respective NMD of the multiple NMDs, wherein the voice input comprises a detected wake-word. Based on respective sound pressure levels of the multiple audio recordings of the voice input, the servers (i) select a particular NMD of the multiple NMDs and (ii) forego selection of other NMDs of the multiple NMDs. The servers send, via the network interface to the particular NMD, data representing a playback command that corresponds to a voice command in the voice input represented in the multiple audio recordings, wherein the data representing the playback command causes the particular NMD to play back audio content according to the playback command.

Disclosed herein are example techniques for voice detection by multiple NMDs. An example implementation may involve one or more servers receiving, via a network interface, data representing multiple audio recordings of a voice input spoken by a given user, each audio recording recorded by a respective NMD of the multiple NMDs, wherein the voice input comprises a detected wake-word. Based on respective sound pressure levels of the multiple audio recordings of the voice input, the servers (i) select a particular NMD of the multiple NMDs and (ii) forego selection of other NMDs of the multiple NMDs. The servers send, via the network interface to the particular NMD, data representing a playback command that corresponds to a voice command in the voice input represented in the multiple audio recordings, wherein the data representing the playback command causes the particular NMD to play back audio content according to the playback command.

Provided is a method of processing a user input to deliver the user input to at least one of a plurality of assistants, includes: converting a user input including a voice signal based on a predetermined rule to generate an instruction; splitting a complex instruction into partial instructions based on that the generated instruction is the complex instruction requesting two or more events; and determining a domain of each of the partial instructions and distributing the partial instructions to at least one of a plurality of voice assistants based on the domain. According to an embodiment, the washer may be related to artificial intelligence (AI) modules, unmanned aerial vehicles (UAVs), robots, augmented reality (AR) devices, virtual reality (VR) devices, and 5G service-related devices.

Provided is a method of processing a user input to deliver the user input to at least one of a plurality of assistants, includes: converting a user input including a voice signal based on a predetermined rule to generate an instruction; splitting a complex instruction into partial instructions based on that the generated instruction is the complex instruction requesting two or more events; and determining a domain of each of the partial instructions and distributing the partial instructions to at least one of a plurality of voice assistants based on the domain. According to an embodiment, the washer may be related to artificial intelligence (AI) modules, unmanned aerial vehicles (UAVs), robots, augmented reality (AR) devices, virtual reality (VR) devices, and 5G service-related devices.

The present disclosure pertains to a system configured to generate output data from a series of configurable cloud-computing processing operations. Some embodiments may; obtain, from a first database, data in a first format; process, in a first stage, the obtained data such that the obtained data is normalized; process, in a second stage, data in a second format different from the first format using a first set of cloud-computing devices; and detect, via a user interface, a first selection of at least two processing operations, the first selection being made from among a plurality of available processing operations. Each of the processing stages may include input data, a processing operation, and output data.

The present disclosure pertains to a system configured to generate output data from a series of configurable cloud-computing processing operations. Some embodiments may; obtain, from a first database, data in a first format; process, in a first stage, the obtained data such that the obtained data is normalized; process, in a second stage, data in a second format different from the first format using a first set of cloud-computing devices; and detect, via a user interface, a first selection of at least two processing operations, the first selection being made from among a plurality of available processing operations. Each of the processing stages may include input data, a processing operation, and output data.

Disclosed are an electronic device capable of efficiently performing speech recognition and natural language understanding and a method for controlling thereof. The electronic device includes: a microphone; a non-volatile memory configured to store virtual assistant model data comprising data that is classified according to a plurality of domains and data that is commonly used for the plurality of domains; a volatile memory; and a processor configured to: based on receiving, through the microphone, a trigger input to perform speech recognition for a user speech, initiate loading the virtual assistant model data from the non-volatile memory into the volatile memory, load, into the volatile memory, first data from among the data classified according to the plurality of domains and, while loading the first data into the volatile memory, load at least a part of the data commonly used for the plurality of domains into the volatile memory.

Disclosed are an electronic device capable of efficiently performing speech recognition and natural language understanding and a method for controlling thereof. The electronic device includes: a microphone; a non-volatile memory configured to store virtual assistant model data comprising data that is classified according to a plurality of domains and data that is commonly used for the plurality of domains; a volatile memory; and a processor configured to: based on receiving, through the microphone, a trigger input to perform speech recognition for a user speech, initiate loading the virtual assistant model data from the non-volatile memory into the volatile memory, load, into the volatile memory, first data from among the data classified according to the plurality of domains and, while loading the first data into the volatile memory, load at least a part of the data commonly used for the plurality of domains into the volatile memory.

Disclosed are devices, systems, apparatuses, methods, products, and other implementations for improving the accuracy and latency in work estimation systems and methods through the invocation of serverless applications and/or servers and the interfacing of natural language processing endpoint devices.