Methods, apparatus, systems, and computer-readable media are provided for providing context specific schema files that allow an automated assistant to broker human-to-computer dialogs between a user and an application that is separate from the automated assistant. The context specific schema file can provide the automated assistant with sufficient data to be responsive to user queries without necessarily communicating with a remote device, such as a server. Multiple different context specific schema files can be made available to the automated assistant according to a context in which a user is interacting with the automated assistant. In this way, latency otherwise exhibited by the automated assistant can be mitigated by providing the automated assistant with the information needed to respond to a user without continually retrieving the information over a network.

The present disclosure provides a method and an apparatus of verifying information based on a voice interaction, a device, and a computer storage medium, and relates to a field of artificial intelligence technology. The present disclosure is implemented to include: acquiring a text of a voice response of a user to a voice inquiry, wherein the voice inquiry is provided for verifying information with the user; and inputting each character of the text of the voice response and a phonetic information associated with the each character to a pre-trained semantic analysis model so as to obtain an user intention information and/or an information of an object to be verified output by the pre-trained semantic analysis model, wherein the user intention information includes a confirmation, a denial, an answer, or a question.

The present disclosure in some embodiments provides a dialogue state tracking method and a dialogue state tracking apparatus in a goal-oriented dialogue system, which track a dialogue state through training based on an attention mechanism between utterances and contextual semantic vectors corresponding respectively to domain-slot types and based on a distance metric-based non-parametric method and thereby facilitate service extension to a multi-domain scenario in the dialogue system.

A method and system is provided for generating a group of phonetically similar masked data. The present application provides a method and system for generating a group of phonetically similar masked data; comprises preprocessing of input dataset values comprising a list of fictitious data values to be used as masked data; determining a plurality of groups of phonetically similar data values present in the dataset list; and deriving metaphone for each input data value to be masked; generating a first numeric code from derived metaphone value of input data value to be masked; selecting one group of phonetically similar data values out of the plurality of groups of phonetically similar data values based on the generated first numeric code; and generating a second numeric code from input data value for selecting a masked value from a plurality of fictitious data group.

Methods and devices for encoding a sparse signal x to generate a compressed encoded signal. The methods employ directionless grammar-based dimension coding. Using labelled subsets and the finding of disjoint repeated subsets in order to build a directionless grammar, the non-zero positions of the sparse signal are encoded in a directionless grammar-based dimension encoder. Element values are encoded in a conditional non-zero encoder. The coding process facilitates random access.

A method, system, and phonetic search engine are described that enable phonetic searches to have increased relevancy to the searcher. Specifically, phonetic searches on a database containing phonetically-searchable content can have one or more phonetically-confusable terms included therein, thereby creating search results that more faithfully reflect the search terms used during the phonetic search of the database.

A data normalization system that normalizes data that includes user names is disclosed herein. The data normalization system converts, using a metaphone algorithm, a first string into a first metaphone string, and a second string into a second metaphone string. The data normalization system searches, based on the first metaphone string and the second metaphone string, a name index including a listing of metaphone strings representing common names and probabilities that the common names are either a given name or a surname. The data normalization system determines, based on searching the name index, a confidence score indicating a confidence level that the first string represents the given name and the second string represents the surname. The data normalization system determines that the confidence score meets or exceeds a threshold confidence score, and in response, determines that the first string represents the given name and the second string represents the surname.

Described herein are techniques, devices, and systems for database management using sound-based identifiers. The sound-based identifiers can be encoded based on text-based identifiers input into one or more databases. The sound-based identifiers can be preprocessed and encoded by encoding the text-based identifiers with a double metaphone algorithm. First sound-based identifiers can be sorted in a cluster associated with a node of a hybrid prefix tree list, based on a longest common prefix of the group. The first sound-based identifiers can be re-encoded as second sound-based identifiers and organized into sub-clusters associated with nodes, based on characters of the second sound-based identifiers positioned after characters associated with the clusters. The re-encoded sound-based identifiers can be determined based on metadata. A query can be received and utilized to identify a re-encoded sound-based identifier. Data associated with the re-encoded sound-based identifier can be transmitted based on the query.

Systems, apparatuses, and methods are provided for identifying a corresponding string stored in memory based on an incomplete input string. A system can analyze and produce phonetic and distance metrics for a plurality of strings stored in memory by comparing the plurality of strings to an incomplete input string. These similarity metrics can be used as the input to a machine learning model, which can quickly and accurately provide a classification. This classification can be used to identify a string stored in memory that corresponds to the incomplete input string.

An augmentative and alternative communication (AAC) system and methods therefore are provided for assisting in learning to read. The system stores book data capturing the sequence of a book and dictionary which is used by a display controller for displaying symbols on an AAC reading user interface. This enables set of symbols to be displayed for each word which include a symbols having a contextually appropriate meaning for the current word being read within a displayed group of symbols. A reader conveys their understanding of the word by selecting the correct symbol. Selecting a symbol triggers audio output of the word associated with the symbol.