Microsoft Technology Licensing, LLC

Jonathan E Hamaker

Christian Liensberger

Michael D Plumpe

Keith Coleman Herold

Varsha Mahadevan

Yifan Gong

Henrik Frystyk Nielsen

Julian James Odell

Shuangyu Chang

Hen Fitoussi

Ye Tian

Mark T Hanson

Harris Syed

Erez Altus

Shiun-Zu Kuo

Qi Miao

Pauline Chao

Yuancheng Tu

Sandeep Manocha

Paul S Bloom

Thomas Reutter

Techniques are described herein that are capable of providing extensibility with regard to a context-aware digital personal assistant. For instance, a description of a capability of a target (e.g., a software target) may be received. Examples of a target include but are not limited to an application (e.g., a software application), a service, a bot, and a website. In a first example, a digital personal assistant may be caused to perform operation(s) based at least in part on a context of a user. In a second example, whether the digital personal assistant performs operation(s) that are configured to be triggered by first aspect(s) of the context of the user is controlled based at least in part on second aspect(s) of the context of the user. The operation(s) include notifying the user of the capability and/or implementing the capability on behalf of the user.

Extensibility for context-aware digital personal assistant

Techniques are described herein that are capable of implementing a context-aware digital personal assistant (DPA) that supports multiple accounts and/or facilitating interaction among digital personal assistants. For example, a user may be signed-in with accounts of a DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least the user's context. In another example, users who are signed-in with accounts of a DPA may share a user experience provided by the DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least one or more of the users' context. In yet another example, a first DPA associated with a first user may be caused to perform an operation on behalf of a second DPA associated with a second user or to delegate the operation to the second DPA.

Facilitating interaction among digital personal assistants

Context-aware digital personal assistant supporting multiple accounts

Techniques and technologies for diagnosing speech recognition errors are described. In an example implementation, a system for diagnosing speech recognition errors may include an error detection module configured to determine that a speech recognition result is least partially erroneous, and a recognition error diagnostics module. The recognition error diagnostics module may be configured to (a) perform a first error analysis of the at least partially erroneous speech recognition result to provide a first error analysis result; (b) perform a second error analysis of the at least partially erroneous speech recognition result to provide a second error analysis result; and (c) determine at least one category of recognition error associated with the at least partially erroneous speech recognition result based on a combination of the first error analysis result and the second error analysis result.

Speech recognition error diagnosis

Textual transcription of speech is generated and formatted according to user-specified transformation and behavior requirements for a speech recognition system having input grammars and transformations. An apparatus may include a speech recognition platform configured to receive a user-specified transformation requirement, recognize speech in speech data into recognized speech according to a set of recognition grammars; and apply transformations to the recognized speech according to the user-specified transformation requirement. The apparatus may further be configured to receive a user-specified behavior requirement and transform the recognized speech according to the behavior requirement. Other embodiments are described and claimed.

Application of user-specified transformations to automatic speech recognition results

Architecture for integrating application-dependent information into a constraints component at deployment time or when available. In terms of a general grammar, the constraints component can include or be a general grammar that comprises application-independent information and is structured in such a way that application-dependent information can be integrated into the general grammar without loss of fidelity. The general grammar includes a probability space and reserves a section of the probability space for the integration of application-dependent information. An integration component integrates the application-dependent information into the reserved section of the probability space for recognition processing. The application-dependent information is integrated into the reserved section of the probability space at deployment time or when available. The general grammar is structured to support the integration and improve the overall system.

Application-dependent information for recognition processing

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Issaquah, WA, United States of America

Jonathan E Hamaker