Google Inc.

Yuli Gao

Sangsoo Sung

Prathab Murugesan

Pedro Jose Moreno Mengibar

Prathab Murugesa

Implementations disclosed herein relate to generating and/or utilizing, by a client device, a text-response map that is stored locally on the client device. The text-response map can include a plurality of mappings, where each of the mappings define a corresponding direct relationship between corresponding text and a corresponding response. Each of the mappings is defined in the text-response map based on the corresponding text being previously generated from previous audio data captured by the client device and based on the corresponding response being previously received from a remote system in response to transmitting, to the remote system, at least one of the previous audio data and the corresponding text.

Rendering responses to a spoken utterance of a user utilizing a local text-response map

A voice to text model used by a voice-enabled electronic device is dynamically and in a context-sensitive manner updated to facilitate recognition of entities that potentially may be spoken by a user in a voice input directed to the voice-enabled electronic device. The dynamic update to the voice to text model may be performed, for example, based upon processing of a first portion of a voice input, e.g., based upon detection of a particular type of voice action, and may be targeted to facilitate the recognition of entities that may occur in a later portion of the same voice input, e.g., entities that are particularly relevant to one or more parameters associated with a detected type of voice action.

Context-sensitive dynamic update of voice to text model in a voice-enabled electronic device

Data associated with a selectively offline capable voice action is locally persisted in a voice-enabled electronic device whenever such an action cannot be competed locally due to the device being offline to enable the action to later be completed after online connectivity has been restored. Synchronization with an online service and/or another electronic device, and/or retrieval of context sensitive data from an online service may be performed after online connectivity has been restored to enable the voice action to thereafter be completed.

Local persisting of data for selectively offline capable voice action in a voice-enabled electronic device

An offline semantic processor of a resource-constrained voice-enabled device such as a mobile device utilizes an offline grammar model with reduced resource requirements to parse voice-based queries received by the device. The offline grammar model may be generated from a larger and more comprehensive grammar model used by an online voice-based query processor, and the generation of the offline grammar model may be based upon query usage data collected from one or more users to enable a subset of more popular voice-based queries from the online grammar model to be incorporated into the offline grammar model. In addition, such a device may collect query usage data and upload such data to an online service to enable an updated offline grammar model to be generated and downloaded back to the device and thereby enable a dynamic update of the offline grammar model to be performed.

Dynamically updatable offline grammar model for resource-constrained offline device

Online processing of a voice input directed to a voice-enabled electronic device is selectively aborted whenever it is determined that a voice input directed to the voice-enabled electronic device can be successfully processed locally by the device. Doing so may in some instances reduce the latency of responding to a voice input.

Selective aborting of online processing of voice inputs in a voice-enabled electronic device

An offline semantic processor of a resource-constrained voice-enabled device such as a mobile device utilizes an offline grammar model with reduced resource requirements to parse voice-based queries. In various implementations, a query issued at a resource-constrained device may be semantically processed to identify candidate responsive actions that are performable by the resource-constrained device. Candidate responsive action performance statistics may be analyzed to select, from the one or more candidate responsive actions, a qualifying responsive action. In various implementations, the candidate responsive action performance statistics may relate to performance of the one or more candidate responsive actions by the resource-constrained device following issuance of the query. An offline grammar model stored in the resource-constrained device may be updated to include a mapping between the issued query and the qualifying responsive action, wherein the offline grammar model maps queries to actions that are performable by the resource-constrained device while offline.

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Sunnyvale, CA, United States of America

Yuli Gao