Abstract: A computing system and related techniques for selecting content to be automatically converted to speech and provided as an audio signal are provided. A text-to-speech request associated with a first document can be received that includes data associated with a playback position of a selector associated with a text-to-speech interface overlaid on the first document. First content associated with the first document can be determined based at least in part on the playback position, the first content including content that is displayed in the user interface at the playback position. The first document can be analyzed to identify one or more structural features associated with the first content. Speech data can be generated based on the first content and the one or more structural features.

Abstract: A computing system and related techniques for selecting content to be automatically converted to speech and provided as an audio signal are provided. A text-to-speech request associated with a first document can be received that includes data associated with a playback position of a selector associated with a text-to-speech interface overlaid on the first document. First content associated with the first document can be determined based at least in part on the playback position, the first content including content that is displayed in the user interface at the playback position. The first document can be analyzed to identify one or more structural features associated with the first content. Speech data can be generated based on the first content and the one or more structural features.

Abstract: Techniques are described herein for leveraging an online semantic processor to generate a finite state machine to be delivered to and implemented on a resource-constrained device. In various implementations, a user request may be received by a personal assistant module. Data indicative of the user request may be uploaded, e.g., by the personal assistant module, to an online semantic processor. Data may be received, e.g., by the personal assistant module, from the online semantic processor. The received data may represent a state machine having a plurality of candidate states of the personal assistant module. Each candidate state may be potentially responsive to the request. Resources local to the resource-constrained device may be analyzed to ascertain signal(s) unavailable to the online semantic processor. The personal assistant module may then transition to a given state of the plurality of candidate states. The given state may be selected based on the signal(s).

Abstract: A computing system and related techniques for selecting content to be automatically converted to speech and provided as an audio signal are provided. A text-to-speech request associated with a first document can be received that includes data associated with a playback position of a selector associated with a text-to-speech interface overlaid on the first document. First content associated with the first document can be determined based at least in part on the playback position, the first content including content that is displayed in the user interface at the playback position. The first document can be analyzed to identify one or more structural features associated with the first content. Speech data can be generated based on the first content and the one or more structural features.

Abstract: Techniques are described herein for leveraging an online semantic processor to generate a finite state machine to be delivered to and implemented on a resource-constrained device. In various implementations, a user request may be received by a personal assistant module. Data indicative of the user request may be uploaded, e.g., by the personal assistant module, to an online semantic processor. Data may be received, e.g., by the personal assistant module, from the online semantic processor. The received data may represent a state machine having a plurality of candidate states of the personal assistant module. Each candidate state may be potentially responsive to the request. Resources local to the resource-constrained device may be analyzed to ascertain signal(s) unavailable to the online semantic processor. The personal assistant module may then transition to a given state of the plurality of candidate states. The given state may be selected based on the signal(s).

Abstract: Techniques are described herein for leveraging an online semantic processor to generate a finite state machine to be delivered to and implemented on a resource-constrained device. In various implementations, a user request may be received by a personal assistant module. Data indicative of the user request may be uploaded, e.g., by the personal assistant module, to an online semantic processor. Data may be received, e.g., by the personal assistant module, from the online semantic processor. The received data may represent a state machine having a plurality of candidate states of the personal assistant module. Each candidate state may be potentially responsive to the request. Resources local to the resource-constrained device may be analyzed to ascertain signal(s) unavailable to the online semantic processor. The personal assistant module may then transition to a given state of the plurality of candidate states. The given state may be selected based on the signal(s).

Abstract: Techniques are described herein for leveraging an online semantic processor to generate a finite state machine to be delivered to and implemented on a resource-constrained device. In various implementations, a user request may be received by a personal assistant module. Data indicative of the user request may be uploaded, e.g., by the personal assistant module, to an online semantic processor. Data may be received, e.g., by the personal assistant module, from the online semantic processor. The received data may represent a state machine having a plurality of candidate states of the personal assistant module. Each candidate state may be potentially responsive to the request. Resources local to the resource-constrained device may be analyzed to ascertain signal(s) unavailable to the online semantic processor. The personal assistant module may then transition to a given state of the plurality of candidate states. The given state may be selected based on the signal(s).