A natural language processing device according to an embodiment of the present disclosure may comprise: a memory for storing a first channel named entity dictionary including basic channel names and a synonym of each of the basic channel names; a communication interface for receiving, from a display device, voice data corresponding to a voice instruction uttered by a user; and a processor which: acquires multiple channel names included in electronic program guide information; extracts channel names matching the acquired multiple channel names from the first channel named entity dictionary so as to acquire a second channel named entity dictionary; acquires the intention of a speech of the voice instruction on the basis of text data of the voice data and the second channel named entity dictionary; and transmits the acquired intention of the speech to the display device through the communication interface.

The method S200 can include: at an aircraft, receiving an audio utterance from air traffic control S210, converting the audio utterance to text, determining commands from the text using a question-and-answer model S240, and optionally controlling the aircraft based on the commands S250. The method functions to automatically interpret flight commands from the air traffic control (ATC) stream.

Systems and processes for intelligent device identification are provided. In one example process, audio input may be sampled with a microphone at each of two or more of the plurality of electronic devices. A first electronic device of the plurality of electronic devices for determining a task associated with sampled audio input may be identified. The process may determine the task based on the sampled audio input with the first electronic device and identify identifying a second electronic device of the plurality of electronic devices for performing the task. The task be performed with the second electronic device. The second electronic device is not the first electronic device in some examples.

Systems for performing intelligent interactive voice recognition functions are provided. In some aspects, natural language data may be received from a plurality of users. The natural language data may be used to train a machine learning model. After training the machine learning model, additional or subsequent natural language input data may be received. The natural language data may include a user query, such as a request to obtain information from the system, to process a transaction, or the like. The natural language data may be processed to remove noise associated with the audio data. The data may then be further processed using the machine learning model to interpret the query of the user and generate an output. The output may be transmitted to the user and feedback data may be received from the user. The user-specific machine learning dataset may then be validated and/or updated based on the feedback data.

Systems for performing intelligent interactive voice recognition functions are provided. In some aspects, natural language data may be received from a plurality of users. The natural language data may be used to generate a plurality of user-specific machine learning datasets. Subsequent natural language input data including a user query may be received. The query may be analyzed to identify the user and a user-specific machine learning dataset associated with the user may be identified. The natural language data may be processed to remove noise associated with the data and may be further processed using the identified user-specific machine learning dataset to interpret the query of the user and generate an output. The output may be transmitted to the user and feedback data may be received from the user. The user-specific machine learning dataset may then be validated and/or updated based on the feedback data.

Systems and methods detecting a spoken sentence in a speech recognition system are disclosed herein. Speech data is buffered based on an audio signal captured at a computing device operating in an active mode. The speech data is buffered irrespective of whether the speech data comprises a signature word. The buffered speech data is processed to detect a presence of the sentence comprising at least one command and a query for the computing device. Processing the buffered speech data includes detecting the signature word in the buffered speech data, and in response to detecting the signature word in the speech data, initiating detection of the sentence in the buffered speech data.

A first electronic device of a local group of connected electronic devices receives a first voice command including a request for a first operation assigns a first target device from among a local group of connected electronic devices as an in-focus device for performing the first operation, causes the first operation to be performed by the first target device via operation of a server-implemented common network service, receives a second voice command including a request for a second operation and based on a determination that the second voice command does not include an explicit designation of a second target device and a determination that the second operation can be performed by the first target device, assigning the first target device.

Disclosed is a method and apparatus for processing a speech. The method includes obtaining context information from a speech signal of a user using a neural network-based encoder, determining, based on the context information, attention information corresponding to a segment included in the speech signal, and recognizing, based on the attention information, the segment by decoding a portion of the context information identified as corresponding to the segment.

A method includes receiving, from an electronic device, information defining a user utterance associated with a skill to be performed, where the skill is not recognized by a natural language understanding (NLU) engine. The method also includes receiving, from the electronic device, information defining one or more actions for performing the skill. The method further includes identifying, using at least one processor, one or more known skills having one or more slots that map to at least one word or phrase in the user utterance. The method also includes creating, using the at least one processor, a plurality of additional utterances based on the one or more mapped slots. In addition, the method includes training, using the at least one processor, the NLU engine using the plurality of additional utterances.

An electronic device according to an embodiment of the disclosure includes: a microphone; a memory storing a plurality of domain sets; and at least one processor electrically connected to the microphone and the memory, wherein the at least one processor is configured to: acquire a voice signal using the microphone; acquire context information associated with at least one of the electronic device or a user; determine a first domain set of the plurality of domain sets based on at least the context information; and perform an operation corresponding to the voice signal based on the determined first domain set.