Abstract: A system for performing automated speech recognition (ASR) on audio data includes a queue manager to receive a request to perform ASR on audio data, add the request to a queue of incoming requests, and determine a queue depth representing a number of requests in the queue at a given time. The system also includes a load supervisor to receive the request and the queue depth from the queue manager and assign a service level for the request based on the queue depth. In addition, the system includes a speech-to-text converter to receive the assigned service level for the request from the load supervisor, select an ASR model for the request based on the received service level, receive the audio data associated with the request, and perform ASR on the audio data using the selected ASR model.

Abstract: A system and method are disclosed capable of parsing a spoken utterance into a natural language request and a speech audio segment, where the natural language request directs the system to use the speech audio segment as a new wakeword. In response to this wakeword assignment directive, the system and method are further capable of immediately building a new wakeword spotter to activate the device upon matching the new wakeword in the input audio. Different approaches to promptly building a new wakeword spotter are described. Variations of wakeword assignment directives can make the new wakeword public or private. They can also add the new wakeword to earlier wakewords, or replace earlier wakewords.

Abstract: A system and method are disclosed for ignoring a wakeword received at a speech-enabled listening device when it is determined the wakeword is reproduced audio from an audio-playing device. Determination can be by detecting audio distortions, by an ignore flag sent locally between an audio-playing device and speech-enabled device, by and ignore flag sent from a server, by comparison of received audio played audio to a wakeword within an audio-playing device or a speech-enabled device, and other means.

Abstract: [Object] To provide a technique for more accurate interpretation of a message inputted by a user. [Solving Means] An information processing server 300 obtains a first message from a user in a thread 001, has a context of the first message stored in a context database 500 in association with the thread 001, obtains a second message from the user in the thread 001, and provides the second message to a conversation server 400 together with the context of the first message.

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: Natural language grammars interpret expressions at the conversational human-machine interfaces of devices. Under conditions favoring engagement, as specified in a unit of conversational code, the device initiates a discussion using one or more of TTS, images, video, audio, and animation depending on the device capabilities of screen and audio output. Conversational code units specify conditions based on conversation state, mood, and privacy. Grammars provide intents that cause calls to system functions. Units can provide scripts for guiding the conversation. The device, or supporting server system, can provide feedback to creators of the conversational code units for analysis and machine learning.

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: 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.