Abstract: Techniques performed by a data processing system for processing voice content received from a user herein include receiving a first audio input from the user comprising a mixed-mode dictation, analyzing, using one or more machine learning (ML) models, the first audio input to obtain a first interpretation of the mixed-mode dictation, presenting the first interpretation to the user in an application on the data processing system, receiving a second audio input from the user comprising a corrective command, analyzing the second audio input to obtain a second interpretation of the restatement of the mixed-mode dictation presenting the second interpretation to the user, receiving an indication from the user that the second interpretation is a correct interpretation of the mixed-mode dictation, and modifying the operating parameters of the one or more machine learning models to interpret the subsequent instances of the mixed-mode dictation based on the second interpretation.

Abstract: Techniques performed by a data processing system for processing voice content received from a user herein include receiving a first audio input from the user comprising a mixed-mode dictation, analyzing, using one or more machine learning (ML) models, the first audio input to obtain a first interpretation of the mixed-mode dictation, presenting the first interpretation to the user in an application on the data processing system, receiving a second audio input from the user comprising a corrective command, analyzing the second audio input to obtain a second interpretation of the restatement of the mixed-mode dictation presenting the second interpretation to the user, receiving an indication from the user that the second interpretation is a correct interpretation of the mixed-mode dictation, and modifying the operating parameters of the one or more machine learning models to interpret the subsequent instances of the mixed-mode dictation based on the second interpretation.

Abstract: A computing system includes at least one processor and memory storing instructions that, when executed, cause the computing system to receive an indication of a set of grammar commands for an application associated with the computing system, each grammar command corresponding to an executable command that is executable by the application to perform a corresponding application action, receive an indication of a first speech input, from a user, captured by a speech capture component, detect an ambiguity based on a correlation between the first speech input and the set of grammar commands, provide, to the user, an output that represents the detected ambiguity, receive, in response to the output, an indication of a second speech input that is captured by the speech capture component and resolves the ambiguity, and, based on the second speech input execute the executable command that corresponds to one of the grammar commands.

Abstract: An electronic device can receive user input via voice or text that includes tasks to be performed. A digital personal assistant infrastructure service can control to which registered action provider the task is assigned. Per-task action provider preferences can be stored. If a preferred action provider is not able to complete the task, the task can still be performed by a registered action provider that has appropriate capabilities. Machine learning can determine a user's preferences. Resource conservation and effective user interaction can result.

Abstract: Techniques and systems to provide speech recognition services over a network using a standard interface are described. In an embodiment, a technique includes accepting a speech recognition request that includes at least audio input, via an application program interface (API). The speech recognition request may also include additional parameters. The technique further includes performing speech recognition on the audio according to the request and any specified parameters; and returning a speech recognition result as a hypertext protocol (HTTP) response. Other embodiments are described and claimed.

Abstract: A method and system for facilitating centralized interaction with a user includes providing a recognized voice command to a plurality of application modules. A plurality of interpretations of the voice command are generated by at least one of the plurality of application modules. A centralized interface module visually renders the plurality of interpretations of the voice command on a centralized display. An indication of selection of an interpretation is received from the user.

Abstract: Techniques are described for headlessly completing a task of an application in the background of a digital personal assistant. For example, a method can include receiving a voice input via a microphone. Natural language processing can be performed using the voice input to determine a user voice command. The user voice command can include a request to perform a task of the application. The application can be caused to execute the task as a background process without a user interface of the application appearing. A user interface of the digital personal assistant can provide a response to the user, based on a received state associated with the task, so that the response comes from within a context of the user interface of the digital personal assistant without surfacing the user interface of the application.

Abstract: Techniques and systems to provide speech recognition services over a network using a standard interface are described. In an embodiment, a technique includes accepting a speech recognition request that includes at least audio input, via an application program interface (API). The speech recognition request may also include additional parameters. The technique further includes performing speech recognition on the audio according to the request and any specified parameters; and returning a speech recognition result as a hypertext protocol (HTTP) response. Other embodiments are described and claimed.

Abstract: Techniques and systems to provide speech recognition services over a network using a standard interface are described. In an embodiment, a technique includes accepting a speech recognition request that includes at least audio input, via an application program interface (API). The speech recognition request may also include additional parameters. The technique further includes performing speech recognition on the audio according to the request and any specified parameters; and returning a speech recognition result as a hypertext protocol (HTTP) response. Other embodiments are described and claimed.

Abstract: An object is associated with one or more controls in a software application. An object associated with a control determines the operation of the data entry user interface when the data entry user interface is being employed to enter data into the control. More particularly, the object may communicate interface e characteristics to a component that is responsible for providing the user interface to the user. Such a component may be, for example, a shared software module that renders the user interface on a display, receives input data from the user through the user interface, and routes the entered data to a designated destination. Alternately, the object itself may create a user interface having the specified characteristics.

Abstract: Techniques and systems to provide speech recognition services over a network using a standard interface are described. In an embodiment, a technique includes accepting a speech recognition request that includes at least audio input, via an application program interface (API). The speech recognition request may also include additional parameters. The technique further includes performing speech recognition on the audio according to the request and any specified parameters; and returning a speech recognition result as a hypertext protocol (HTTP) response. Other embodiments are described and claimed.

Abstract: Techniques and systems to provide speech recognition services over a network using a standard interface are described. In an embodiment, a technique includes accepting a speech recognition request that includes at least audio input, via an application program interface (API). The speech recognition request may also include additional parameters. The technique further includes performing speech recognition on the audio according to the request and any specified parameters; and returning a speech recognition result as a hypertext protocol (HTTP) response. Other embodiments are described and claimed.

Abstract: A method and system for facilitating centralized interaction with a user includes providing a recognized voice command to a plurality of application modules. A plurality of interpretations of the voice command are generated by at least one of the plurality of application modules. A centralized interface module visually renders the plurality of interpretations of the voice command on a centralized display. An indication of selection of an interpretation is received from the user.

Abstract: Techniques are described for headlessly completing a task of an application in the background of a digital personal assistant. For example, a method can include receiving a voice input via a microphone. Natural language processing can be performed using the voice input to determine a user voice command. The user voice command can include a request to perform a task of the application. The application can be caused to execute the task as a background process without a user interface of the application appearing. A user interface of the digital personal assistant can provide a response to the user, based on a received state associated with the task, so that the response comes from within a context of the user interface of the digital personal assistant without surfacing the user interface of the application.

Abstract: An electronic device can receive user input via voice or text that includes tasks to be performed. A digital personal assistant infrastructure service can control to which registered action provider the task is assigned. Per-task action provider preferences can be stored. If a preferred action provider is not able to complete the task, the task can still be performed by a registered action provider that has appropriate capabilities. Machine learning can determine a user's preferences. Resource conservation and effective user interaction can result.

Abstract: A method and system for facilitating centralized interaction with a user includes providing a recognized voice command to a plurality of application modules. A plurality of interpretations of the voice command are generated by at least one of the plurality of application modules. A centralized interface module visually renders the plurality of interpretations of the voice command on a centralized display. An indication of selection of an interpretation is received from the user.

Abstract: A method and system for facilitating centralized interaction with a user includes providing a recognized voice command to a plurality of application modules. A plurality of interpretations of the voice command are generated by at least one of the plurality of application modules. A centralized interface module visually renders the plurality of interpretations of the voice command on a centralized display. An indication of selection of an interpretation is received from the user.

Abstract: Techniques to provide automatic speech recognition at a local device are described. An apparatus may include an audio input to receive audio data indicating a task. The apparatus may further include a local recognizer component to receive the audio data, to pass the audio data to a remote recognizer while receiving the audio data, and to recognize speech from the audio data. The apparatus may further include a federation component operative to receive one or more recognition results from the local recognizer and/or the remote recognizer, and to federate a plurality of recognition results to produce a most likely result. The apparatus may further include an application to perform the task indicated by the most likely result. Other embodiments are described and claimed.

Abstract: An object is associated with one or more controls in a software application. An object associated with a control determines the operation of the data entry user interface when the data entry user interface is being employed to enter data into the control. More particularly, the object may communicate interface e characteristics to a component that is responsible for providing the user interface to the user. Such a component may be, for example, a shared software module that renders the user interface on a display, receives input data from the user through the user interface, and routes the entered data to a designated destination. Alternately, the object itself may create a user interface having the specified characteristics.

Abstract: Semantically distinct items are extracted from a single utterance by repeatedly recognizing the same utterance using constraints provided by semantic items already recognized. User feedback for selection or correction of partially recognized utterance may be used in a hierarchical, multi-modal, or single step manner. An accuracy of recognition is preserved while the less structured and more natural single utterance recognition form is allowed to be used.