Abstract: Methods and systems for automatically generating sample phrases or sentences that a user can say to invoke a set of defined actions performed by a virtual assistant are disclosed. By enabling finetuned general-purpose natural language models, the system can generate potential and accurate utterance sentences based on extracted keywords or the input utterance sentence. Furthermore, domain-specific datasets can be used to train the pre-trained, general-purpose natural language models via unsupervised learning. These generated sentences can improve the efficiency of configuring a virtual assistant. The system can further optimize the effectiveness of a virtual assistant in understanding the user, which can enhance the user experience of communicating with it.

Abstract: Developers can configure custom acoustic models by providing audio files with custom recordings. The custom acoustic model is trained by tuning a baseline model using the audio files. Audio files may contain custom noise to apply to clean speech for training. The custom acoustic model is provided as an alternative to a standard acoustic model. Device developers can select an acoustic model by a user interface. Speech recognition is performed on speech audio using one or more acoustic models. The result can be provided to developers through the user interface, and an error rate can be computed and also provided.

Abstract: A user request is received (e.g., in natural language form) by a client device. In order to facilitate richer natural language understanding, a response-processing server handles interpretation of the request, rather than requiring the client device to interpret it. The response-processing server determines the various possible responses that client devices could make in response to the request based on (for example) the state of the application data, and/or the capabilities of the client devices. The response-processing server accordingly a response package that describes a number of different conditional responses that client devices could have to the request. The client device selects a response from the response package, executes the command (if possible), and provides the user with some representation of the response.

Abstract: A method of controlling an engagement state of an agent during a human-machine dialog is provided. The method can include receiving a spoken request that is a conditional locking request, wherein the conditional locking request uses a natural language expression to explicitly specify a locking condition, which is a predicate, storing the predicate in a format that can be evaluated when needed by the agent, entering a conditionally locked state in response to the conditional locking request, in the conditionally locked state, receiving a multiplicity of requests without a need for a wakeup indicator, and for a request from the multiplicity of requests evaluating the predicate upon receiving the request, and processing the request if the predicate is true.

Abstract: A method is described that includes processing text and speech from an input utterance using local overrides of default dictionary pronunciations. Applying this method, a word-level grammar used to process the tokens specifies at least one local word phonetic variant that applies within a specific production rule and, within a local context of the specific production rule, the local word phonetic variant overrides one or more default dictionary phonetic versions of the word. This method can be applied to parsing utterances where the pronunciation of some words depends on their syntactic or semantic context.

Abstract: Agents engage and disengage with users intelligently. Users can tell agents to remain engaged without requiring a wakeword. Engaged states can support modal dialogs and barge-in. Users can cause disengagement explicitly. Disengagement can be conditional based on timeout, change of user, or environmental conditions. Engagement can be one-time or recurrent. Recurrent states can be attentive or locked. Locked states can be unconditional or conditional, including being reserved to support user continuity. User continuity can be tested by matching parameters or tracking user by many modalities including microphone arrays, cameras, and other sensors.

Abstract: A client device receives a user request (e.g., in natural language form) to execute a command of an application. The client device delegates interpretation of the request to a response-processing server. Using domain knowledge previously provided by a developer of the application, the response-processing server determines the various possible responses that client devices could make in response to the request based on circumstances such as the capabilities of the client devices and the state of the application data. The response-processing server accordingly generates a response package that describes a number of different conditional responses that client devices could have to the request and provides the response package to the client device. The client device selects the appropriate response from the response package based on the circumstances as determined by the client device, executes the command (if possible), and provides the user with some representation of the response.

Abstract: A command-processing server receives a natural language command from a user. The command-processing server has a set of domain command interpreters corresponding to different domains in which commands can be expressed, such as the domain of entertainment, or the domain of travel. Some or all of the domain command interpreters recognize user commands having a verbal prefix, an optional pre-filter, an object, and an optional post-filter; the pre- and post-filters may be compounded expressions involving multiple atomic filters. Different developers may independently specify the domain command interpreters and the sub-structure interpreters on which they are based.

Abstract: A machine learning system for a digital assistant is described, together with a method of training such a system. The machine learning system is based on an encoder-decoder sequence-to-sequence neural network architecture trained to map input sequence data to output sequence data, where the input sequence data relates to an initial query and the output sequence data represents canonical data representation for the query. The method of training involves generating a training dataset for the machine learning system. The method involves clustering vector representations of the query data samples to generate canonical-query original-query pairs in training the machine learning system.

Abstract: The present invention extends to methods, systems, and computer program products for interpreting queries according to preferences. Multi-domain natural language understanding systems can support a variety of different types of clients. Queries can be received and interpreted across one or more domains. Preferred query interpretations can be identified and query responses provided based on any of: domain preferences, preferences indicated by an identifier, or (e.g., weighted) scores exceeding a threshold.

Abstract: Aspects include methods, systems, and computer-program products providing virtual assistant domain functionality. A natural language query including one or more words is received. A collection of natural language modules is accessed. The collection natural language modules are configured to process sets of natural language queries. A natural language module, from the collection of natural language modules, is identified to interpret the natural language query. An interpretation of the natural language query is computed using the identified natural language module. A response to the natural language query is returned using the computed interpretation.

Abstract: A method and system for acoustic model conditioning on non-phoneme information features for optimized automatic speech recognition is provided. The method includes using an encoder model to encode sound embedding from a known key phrase of speech and conditioning an acoustic model with the sound embedding to optimize its performance in inferring the probabilities of phonemes in the speech. The sound embedding can comprise non-phoneme information related to the key phrase and the following utterance. Further, the encoder model and the acoustic model can be neural networks that are jointly trained with audio data.

Abstract: A platform provides for developers of applications, such as devices, with natural language interfaces to configure the availability of vertical domain modules in applications. Modules can include grammars for parsing natural language expressions and interfaces to data sources. Third party developers can create modules with pricing models for their usage or access to their data. Device developers can browse or search available modules and test their performance for specific queries. The platform provides for devices users to access the chosen modules as configured by device developers and for charging and payment between users, application developers, and module developers.

Abstract: A query-processing server provides natural language services to applications. More specifically, the query-processing server receives and stores domain knowledge information from application developers, the domain knowledge information comprising a linguistic description of the natural language user queries that application developers wish their applications to support. A first portion of the domain knowledge information is applied to transform a natural language query received from an application to an ordered sequence of question elements. A second portion of the domain knowledge information is applied to group the ordered sequence of question elements into a plurality of distinct structured questions posed by the natural language query. The distinct structured questions may then be provided to the application, which may then execute them and obtain the corresponding data referenced by the questions.

Abstract: A domain-independent framework parses and interprets compound natural language queries in the context of a conversation between a human and an agent. Generic grammar rules and corresponding semantics support the understanding of compound queries in the conversation context. The sub-queries themselves are from one or more domains, and they are parsed and interpreted by a pre-existing grammar, covering one or more pre-existing domains. The pre-existing grammar, extended by the generic rules, recognizes all compound queries based on any queries recognized by the pre-existing grammar. Use of the disclosed framework requires little or no change in the domain-specific NLU handling code. The framework defines a generic approach to propagating context data between sub-queries of a compound query. The framework can be further extended to propagate intra-query context data in, out and across query components.

Abstract: The present invention extends to methods, systems, and computer program products for interpreting expressions having potentially ambiguous meanings in different domains. Multi-domain natural language understanding systems can support a variety of different types of clients. Expressions can be interpreted across multiple domains. Weights can be assigned to domains. Weights can be client specific or expression specific so that a chosen interpretation is more likely correct for the type of client or for its context. Stored weight sets can be chosen according to identifying information carried as metadata with expressions or weight sets carried directly as metadata. Domains can additionally or alternatively be ranked in ordered lists or comparative domain pairs of to favor some domains over others as appropriate for client type or client context.

Abstract: A query-processing server that interprets natural language expressions supports the extension of a first semantic grammar (for a particular type of expression), which is declared extensible, by a second semantic grammar (for another type of expression). When an extension is requested, the query-processing server checks that the two semantic grammars have compatible semantic types. The developers need not have any knowledge of each other, or about their respective grammars. Performing an extension may be done by yet another party, such as the query-processing server, or another server, independently of all previous parties. The use of semantic grammar extensions provides a way to expand the coverage and functionality of natural language interpretation in a simple and flexible manner, so that new forms of expression may be supported, and seamlessly combined with pre-existing interpretations. Finally, in some implementations, this is done without loss of efficiency.

Abstract: A method of providing a platform for configuring device-specific speech recognition is provided. The method includes providing a user interface for developers to select a set of at least two acoustic models appropriate for a specific type of a device, receiving, from a developer, a selection of the set of the at least two acoustic models, and configuring a speech recognition system to perform device-specific speech recognition by using one acoustic model selected from the at least two acoustic models of the set.

Abstract: A method of building a natural language understanding application is provided. The method includes receiving at least one electronic record containing programming code and creating executable code from the programming code. Further, the executable code, when executed by a processor, causes the processor to create a parse and an interpretation of a sequence of input tokens, the programming code includes an interpret-block and the interpret-block includes an interpret-statement. Additionally, the interpret-statement includes a pattern expression and the interpret-statement includes an action statement.

Abstract: A method for processing a natural language query. The method includes receiving a text query, the query referring to a plurality of objects, attributes, qualifiers and other arguments and parsing the query to produce an argument tree representing the substance and structure of the query. The method also includes the capability to define qualifiers as being possibly projectable onto other arguments and indicate their direction of projectability and the capability to denote nodes of the argument tree as foldable, as splittable, or as containing sequences of qualifier arguments. The method additionally includes defining validity rules for a domain of knowledge, used to determine whether a list of arguments form a valid granular query component and processing of the argument tree, in view of the above in order to derive a corresponding plurality of granular query components that collectively request the plurality of pieces of information representing the intent of the query.