Abstract: Technical solutions relate to the fields of artificial intelligence technologies and voice technologies. A technical solution includes: performing voice recognition and demand analysis on a voice instruction input by a user; in response to an unknown demand obtained by the demand analysis, acquiring information of a query entity and query content using a result of the demand analysis, and acquiring reply information corresponding to the query content by communication with the query entity; and returning a first voice response to the user using the reply information.

Abstract: Provided are a query rewriting method and apparatus, a device and a storage medium, relating to the technical field of data processing and, in particular, to technical fields including artificial intelligence, speech technology, intelligent search and deep learning. The solution includes, in response to a query rewriting request, extracting at least one of context information of an original query and intention information of the original query; and determining a new query based on a machine vocabulary collection and the at least one of the context information and the intention information. The understanding degree to the new query by a machine is greater than the understanding degree to the original query by the machine.

Abstract: Provided are a voice packet recommendation method and apparatus, a device and a storage medium, relating to intelligent search technologies. The solution includes constructing a first video training sample according to first user behavior data of a first sample user in a video recommendation scenario and first video data associated with the first user behavior data; constructing a user training sample according to sample search data of the first sample user and historical interaction data about a first sample voice packet; pretraining a neural network model according to the first video training sample and the user training sample; and retraining the pretrained neural network model by using a sample video and sample tag data which are associated with a second sample voice packet to obtain a voice packet recommendation model. With the solution, the neural network model can be trained in the case of cold start so that the neural network model can recommend a voice packet automatically in the case of cold start.

Abstract: A route planning method includes: receiving a route planning request including information of a starting position and information of a destination position; planning a route from the starting position to the destination position based on conditions about a road including a physical isolation belt; sending a route planning result, the conditions about a road including a physical isolation belt are fused into the route planning.

Abstract: Provided are a voice packet recommendation method and apparatus, a device and a storage medium. The method includes selecting at least one target display video for a user from among candidate display videos associated with voice packets and using voice packets to which the at least one target display video belongs as candidate voice packets; selecting a target voice packet for the user from among the candidate voice packets according to attribute information of the candidate voice packets and attribute information of the at least one target display video; and recommending the target voice packet to the user.

Abstract: The present disclosure discloses a broadcast style determination method and apparatus, a device and a computer storage medium, and relates to voice and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: performing named entity recognition on broadcast text to obtain at least one named entity; acquiring domain knowledge corresponding to the at least one named entity; and performing sentiment analysis by using the broadcast text and the domain knowledge, to determine a broadcast style of the broadcast text.

Abstract: Technical solution relates to the fields of voice technologies and knowledge graph technologies. A technical solution includes: acquiring script matched with a scenario from a speech package, and acquiring a broadcast template configured for the scenario in advance; and filling the broadcast template with the script to generate the broadcast speech.

Abstract: A method, an apparatus, an electronic device and a storage medium for broadcasting a voice are provided. The method may include: sending a voice broadcast request to a server, where the voice broadcast request includes at least one of scenario information, user information or voice packet setting information; receiving a voice broadcast instruction corresponding to the voice broadcast request returned by the server; and acquiring a personalized voice packet corresponding to the voice broadcast instruction in a local database and broadcasting the personalized voice packet.

Abstract: The present disclosure provides a method of building a sorting model, and an application method and apparatus based on the model. The method of building a sorting model comprises: obtaining, from a search log, a query including a relationship triple and a clicked title of a search result corresponding to the query, wherein the relationship triple includes a content word pair and a relationship word of the content word pair; obtaining training data using the obtained query, the clicked title corresponding to the query, and times of click of the clicked title; using the training data to train a neural network-based sorting model, the sorting model being used to sort sentences according to the sentences' description of a relationship of the content word pair.

Abstract: The present application discloses a method and apparatus for guiding a voice-packet recording function, a device and a computer storage medium, and relates to the field of big data. An implementation includes identifying a target user with a voice-packet recording demand using historical map usage behaviors of map users; and identifying a scenario where the target user uses a client terminal, and if the scenario conforms to a voice-packet recording scenario, sending first guidance information of the voice-packet recording function to the client terminal. With the present application, accurate guidance of the voice-packet recording function is realized, and excessive disturbance in the user is reduced.

Abstract: The present disclosure provides methods, computing devices, and storage media for generating a training corpus. The method includes: mining out pieces of data from user behavior logs associated with a target application, each piece of data including a first behavior log and a second behavior log, the first behavior log including a user speech and a corresponding speech recognition result, the second behavior log belonging to the same user as the first behavior log and time-dependent with the first behavior log; and determining the user speech and the corresponding speech recognition result in each piece of data as a positive feedback sample or a negative feedback sample, based on the first behavior log and the second behavior log.

Abstract: The present application discloses a method and apparatus for processing voices, a device and a computer storage medium, and relates to the technical field of voices. An implementation includes: recognizing a received voice request by a server of a first voice assistant to obtain a text request; sending the recognized text request to a server of a second voice assistant; receiving token information generated and returned by the server of the second voice assistant for the text request; and sending the text request and the token information to a client of the first voice assistant, such that the client of the first voice assistant calls a client of the second voice assistant to respond to the text request based on the token information. Based on the present application, after a user inputs the voice request with the first voice assistant, the first voice assistant may call the second voice assistant to respond to the voice request when the second voice assistant may better respond to the voice request.

Abstract: A method, apparatus, device and computer storage medium for processing voices, which relate to the technical field of voices, are disclosed. An implementation includes: recognizing a voice request received by a first voice assistant to obtain a text request; determining information of a second voice assistant which is able to process the text request; and calling the second voice assistant to respond to the text request. With the technical solution, when the first voice assistant is not specialized in some fields, the second voice assistant which is specialized in the fields may be called to perform a specific response processing operation, thereby meeting user requirements more accurately.

Abstract: A method, an apparatus, an electronic device and a storage medium for broadcasting a voice are provided. The method may include: sending a voice broadcast request to a server, where the voice broadcast request includes at least one of scenario information, user information or voice packet setting information; receiving a voice broadcast instruction corresponding to the voice broadcast request returned by the server; and acquiring a personalized voice packet corresponding to the voice broadcast instruction in a local database and broadcasting the personalized voice packet.

Abstract: Route planning method, apparatus, device and computer storage medium which relate to the field of big data are disclosed. The route planning method includes: receiving a route planning request including information of a starting position and information of a destination position; planning a route from the starting position to the destination position based on conditions about a road including a physical isolation belt; sending a route planning result. In the present disclosure, since the physical isolation belt on the road affects the user's safety during travel, the conditions about a road including a physical isolation belt are fused into the route planning, thereby improving the safety of the planned route.

Abstract: The present disclosure provides methods, computing devices, and storage media for generating a training corpus. The method includes: mining out pieces of data from user behavior logs associated with a target application, each piece of data including a first behavior log and a second behavior log, the first behavior log including a user speech and a corresponding speech recognition result, the second behavior log belonging to the same user as the first behavior log and time-dependent with the first behavior log; and determining the user speech and the corresponding speech recognition result in each piece of data as a positive feedback sample or a negative feedback sample, based on the first behavior log and the second behavior log.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for recommending entity. A method for recommending entity includes: acquiring a candidate entity set associated with a to-be-searched entity, in response to receiving a user's search request for an entity; inputting the candidate entity set into a pre-trained ranking model to obtain a candidate entity sequence; and selecting a candidate entity from the candidate entity sequence and recommending the selected candidate entity to the user. The ranking model ranks the candidate entity set based on at least one of: a degree of correlation between each candidate entity in the candidate entity set and the to-be-searched entity; a degree of interest of the user in the each candidate entity in the candidate entity set; and a degree of expectation of the user for the each candidate entity in the candidate entity set.

Abstract: The present disclosure provides a method of building a sorting model, and an application method and apparatus based on the model. The method of building a sorting model comprises: obtaining, from a search log, a query including a relationship triple and a clicked title of a search result corresponding to the query, wherein the relationship triple includes a content word pair and a relationship word of the content word pair; obtaining training data using the obtained query, the clicked title corresponding to the query, and times of click of the clicked title; using the training data to train a neural network-based sorting model, the sorting model being used to sort sentences according to the sentences' description of a relationship of the content word pair.