Abstract: Custom acoustic models can be configured by developers 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. A speech recognition system can select an acoustic model for use upon receiving metadata about the device conditions or type. 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 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 neural speech-to-meaning system is trained on speech audio expressing specific intents. The system receives speech audio and produces indications of when the speech in the audio matches the intent. Intents may include variables that can have a large range of values, such as the names of places. The neural speech-to-meaning system simultaneously recognizes enumerated values of variables and general intents. Recognized variable values can serve as arguments to API requests made in response to recognized intents. Accordingly, neural speech-to-meaning supports voice virtual assistants that serve users based on API hits.

Abstract: Support for natural language expressions is provided by the use of semantic grammars that describe the structure of expressions in that grammar and that construct the meaning of a corresponding natural language expression. A semantic grammar extension mechanism is provided, which allows one semantic grammar to be used in the place of another semantic grammar. This enriches the expressivity of semantic grammars in a simple, natural, and decoupled manner.

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 system and method invoke virtual assistant action, which may comprise an argument. From audio, a probability of an intent is inferred. A probability of a domain and a plurality of variable values may also be inferred. Invoking the action is in response to the intent probability exceeding a threshold. Invoking the action may also be in response to the domain probability exceeding a threshold, a variable value probability exceeding a threshold, detecting an end of utterance, and a specific amount of time having elapsed. The intent probability may increase when the audio includes speech of words with the same meaning in multiple natural languages. Invoking the action may also be conditional on the variable value exceeding its threshold within a certain period of time of the intent probability exceeding its threshold.

Abstract: A neural speech-to-meaning system is trained on speech audio expressing specific intents. The system receives speech audio and produces indications of when the speech in the audio matches the intent. Intents may include variables that can have a large range of values, such as the names of places. The neural speech-to-meaning system simultaneously recognizes enumerated values of variables and general intents. Recognized variable values can serve as arguments to API requests made in response to recognized intents. Accordingly, neural speech-to-meaning supports voice virtual assistants that serve users based on API hits.