An electronic apparatus is provided. The electronic apparatus includes a microphone, a memory configured to store a plurality of keyword recognition models, and a processor, which is coupled with the microphone and the memory, configured to control the electronic apparatus, wherein the processor is configured to selectively execute at least one keyword recognition model among the plurality of keyword recognition models based on operating state information of the electronic apparatus, based on a first user voice being input through the microphone, identify whether at least one keyword corresponding to the executed keyword recognition model is included in the first user voice by using the executed keyword recognition model, and based on at least one keyword identified as being included in the first user voice, perform an operation of the electronic apparatus corresponding to the at least one keyword.

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.

A method and device for compressing FST data are provided. The method includes: acquiring to-be-compressed FST data, where the FST data includes state transition data and state data; decomposing the state transition data based on first data categories to acquire first decomposition data; decomposing the state data based on second data categories to acquire second decomposition data; sequentially arranging, for each of the first data categories, the first decomposition data of the first data category, to acquire first arrangement data of the first data category; alternately arranging the first arrangement data and the second decomposition data according to a sequential order used in the first arrangement data, to acquire second arrangement data; performing classification statistics on the first arrangement data and the second arrangement data to acquire index data; and combining the first arrangement data, the second arrangement data, and the index data, to obtain the compressed FST data.

A method and device for compressing FST data are provided. The method includes: acquiring to-be-compressed FST data, where the FST data includes state transition data and state data; decomposing the state transition data based on first data categories to acquire first decomposition data; decomposing the state data based on second data categories to acquire second decomposition data; sequentially arranging, for each of the first data categories, the first decomposition data of the first data category, to acquire first arrangement data of the first data category; alternately arranging the first arrangement data and the second decomposition data according to a sequential order used in the first arrangement data, to acquire second arrangement data; performing classification statistics on the first arrangement data and the second arrangement data to acquire index data; and combining the first arrangement data, the second arrangement data, and the index data, to obtain the compressed FST data.

In some implementations, a system may receive non-deterministic finite state automata (NFSA) to represent a set of dialog flows associated with a human-machine interface. The system may generate a deterministic finite state automaton (DFSA) that includes a minimum set of states that represents all dialog flows included in the set of dialog flows represented in the NFSA and does not represent any dialog flows that are not included in the set of dialog flows represented in the NFSA. The system may traverse the DFSA to identify a set of K paths that have a highest total weight based on a weight assigned to each transition in the DFSA. The system may prune the DFSA to remove any states and any transitions that do not belong to the set of K paths. The system may generate an output related to one or more subsets of the set of K paths.

In some implementations, a system may receive non-deterministic finite state automata (NFSA) to represent a set of dialog flows associated with a human-machine interface. The system may generate a deterministic finite state automaton (DFSA) that includes a minimum set of states that represents all dialog flows included in the set of dialog flows represented in the NFSA and does not represent any dialog flows that are not included in the set of dialog flows represented in the NFSA. The system may traverse the DFSA to identify a set of K paths that have a highest total weight based on a weight assigned to each transition in the DFSA. The system may prune the DFSA to remove any states and any transitions that do not belong to the set of K paths. The system may generate an output related to one or more subsets of the set of K paths.

Systems and methods described herein relate to adapting a language model for automatic speech recognition (ASR) for a new set of words. Instead of retraining the ASR models, language models and grammar models, the system only modifies one grammar model and ensures its compatibility with the existing models in the ASR system.

Systems and methods described herein relate to adapting a language model for automatic speech recognition (ASR) for a new set of words. Instead of retraining the ASR models, language models and grammar models, the system only modifies one grammar model and ensures its compatibility with the existing models in the ASR system.

According to one embodiment, an information processing apparatus includes a processor. The processor generates a template, regarding a recording data sheet including a plurality of items, for one or more of the items that can be specified, with reference to an input order of input target items selected from the items. The processor performs a speech recognition on an utterance of a user and generate a speech recognition result. The processor determines an input target range relating to one more items specified by the utterance of the user among the items based on the template and the speech recognition result.