In one embodiment, a domain-name based framework implemented in a digital assistant ecosystem uses domain names as unique identifiers for request types, requesting entities, responders, and target entities embedded in a natural language request. Further, the framework enables interpreting natural language requests according to domain ontologies associated with different responders. A domain ontology operates as a keyword dictionary for a given responder and defines the keywords and corresponding allowable values to be used for request types and request parameters. The domain-name based framework thus enables the digital assistant to interact with any responder that supports a domain ontology to generate precise and complete responses to natural language based requests.

In one embodiment, a domain-name based framework implemented in a digital assistant ecosystem uses domain names as unique identifiers for request types, requesting entities, responders, and target entities embedded in a natural language request. Further, the framework enables interpreting natural language requests according to domain ontologies associated with different responders. A domain ontology operates as a keyword dictionary for a given responder and defines the keywords and corresponding allowable values to be used for request types and request parameters. The domain-name based framework thus enables the digital assistant to interact with any responder that supports a domain ontology to generate precise and complete responses to natural language based requests.

According to one embodiment, a speech recognition error correction apparatus includes a correction network memory and an error correction circuitry. The error correction circuitry calculates a difference between a speech recognition result string of an error correction target, which is a result of performing speech recognition on a new series of speech data, and a correction network, where a speech recognition result string and a correction result by a user for the speech recognition result string are associated, and when a value indicating the difference is equal to or less than a threshold, perform error correction on a speech recognition error portion in the speech recognition result string of the error correction target by using the correction network to generate a speech recognition error correction result string.

A data processing system for artificial intelligence-based setting of controls in an evaluation interface comprising a data store storing: a plurality of transactions; a plurality of completed evaluations, each completed evaluation including an indication of a transcript portion associated with an evaluation answer. The system determines a word or phrase common to a first set of transcript portions associated with the evaluation answer; creates a first set of auto answer parameters that includes the word or phrase; auto answers the question for a set of test transactions to generate an auto answer for each test transaction; and based on a determination that the first set of auto answer parameters auto answered the question with a threshold level of accuracy, configures an evaluation system to use the first set of auto answer parameters to preset an answer control in an evaluation operator interface.

A data processing system for artificial intelligence-based setting of controls in an evaluation interface comprising a data store storing: a plurality of transactions; a plurality of completed evaluations, each completed evaluation including an indication of a transcript portion associated with an evaluation answer. The system determines a word or phrase common to a first set of transcript portions associated with the evaluation answer; creates a first set of auto answer parameters that includes the word or phrase; auto answers the question for a set of test transactions to generate an auto answer for each test transaction; and based on a determination that the first set of auto answer parameters auto answered the question with a threshold level of accuracy, configures an evaluation system to use the first set of auto answer parameters to preset an answer control in an evaluation operator interface.

A system includes acquisition of a domain grammar, determination of an interpolated grammar based on the domain grammar and a base grammar, determination of a delta domain grammar based on an augmented first grammar and the interpolated grammar, determination of an out-of-vocabulary class based on the domain grammar and the base grammar, insertion of the out-of-vocabulary class into a composed transducer composed of the augmented first grammar and one or more other transducers to generate an updated composed transducer, composition of the delta domain grammar and the updated composed transducer, and application of the composition of the delta domain grammar and the updated composed transducer to an output of an acoustic model.

A system includes acquisition of a domain grammar, determination of an interpolated grammar based on the domain grammar and a base grammar, determination of a delta domain grammar based on an augmented first grammar and the interpolated grammar, determination of an out-of-vocabulary class based on the domain grammar and the base grammar, insertion of the out-of-vocabulary class into a composed transducer composed of the augmented first grammar and one or more other transducers to generate an updated composed transducer, composition of the delta domain grammar and the updated composed transducer, and application of the composition of the delta domain grammar and the updated composed transducer to an output of an acoustic model.

Described herein is a neural network accelerator (NNA) with a decompression unit that can be configured to perform multiple types of decompression. The decompression may include a separate subunit for each decompression type. The subunits can be coupled to form a pipeline in which partially decompressed results generated by one subunit are input for further decompression by another subunit. Depending on which types of compression were applied to incoming data, any number of the subunits may be used to produce a decompressed output. In some embodiments, the decompression unit is configured to decompress data that has been compressed using a zero value compression scheme, a shared value compression scheme, or both. The NNA can also include a compression unit implemented in a manner similar to that of the decompression unit.

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.

In one aspect, a method comprises accessing audio data generated by a computing device based on audio input from a user, the audio data encoding one or more user utterances. The method further comprises generating a first transcription of the utterances by performing speech recognition on the audio data using a first speech recognizer that employs a language model based on user-specific data. The method further comprises generating a second transcription of the utterances by performing speech recognition on the audio data using a second speech recognizer that employs a language model independent of user-specific data. The method further comprises determining that the second transcription of the utterances includes a term from a predefined set of one or more terms. The method further comprises, based on determining that the second transcription of the utterance includes the term, providing an output of the first transcription of the utterance.