Abstract: Implementations relate to dynamically, and in a context-sensitive manner, biasing voice to text conversion. In some implementations, the biasing of voice to text conversions is performed by a voice to text engine of a local agent, and the biasing is based at least in part on content provided to the local agent by a third-party (3P) agent that is in network communication with the local agent. In some of those implementations, the content includes contextual parameters that are provided by the 3P agent in combination with responsive content generated by the 3P agent during a dialog that: is between the 3P agent, and a user of a voice-enabled electronic device; and is facilitated by the local agent. The contextual parameters indicate potential feature(s) of further voice input that is to be provided in response to the responsive content generated by the 3P agent.

Abstract: Implementations relate to dynamically, and in a context-sensitive manner, biasing voice to text conversion. In some implementations, the biasing of voice to text conversions is performed by a voice to text engine of a local agent, and the biasing is based at least in part on content provided to the local agent by a third-party (3P) agent that is in network communication with the local agent. In some of those implementations, the content includes contextual parameters that are provided by the 3P agent in combination with responsive content generated by the 3P agent during a dialog that: is between the 3P agent, and a user of a voice-enabled electronic device; and is facilitated by the local agent. The contextual parameters indicate potential feature(s) of further voice input that is to be provided in response to the responsive content generated by the 3P agent.

Abstract: Implementations relate to dynamically, and in a context-sensitive manner, biasing voice to text conversion. In some implementations, the biasing of voice to text conversions is performed by a voice to text engine of a local agent, and the biasing is based at least in part on content provided to the local agent by a third-party (3P) agent that is in network communication with the local agent. In some of those implementations, the content includes contextual parameters that are provided by the 3P agent in combination with responsive content generated by the 3P agent during a dialog that: is between the 3P agent, and a user of a voice-enabled electronic device; and is facilitated by the local agent. The contextual parameters indicate potential feature(s) of further voice input that is to be provided in response to the responsive content generated by the 3P agent.

Abstract: Implementations relate to dynamically, and in a context-sensitive manner, biasing voice to text conversion. In some implementations, the biasing of voice to text conversions is performed by a voice to text engine of a local agent, and the biasing is based at least in part on content provided to the local agent by a third-party (3P) agent that is in network communication with the local agent. In some of those implementations, the content includes contextual parameters that are provided by the 3P agent in combination with responsive content generated by the 3P agent during a dialog that: is between the 3P agent, and a user of a voice-enabled electronic device; and is facilitated by the local agent. The contextual parameters indicate potential feature(s) of further voice input that is to be provided in response to the responsive content generated by the 3P agent.

Abstract: Implementations relate to dynamically, and in a context-sensitive manner, biasing voice to text conversion. In some implementations, the biasing of voice to text conversions is performed by a voice to text engine of a local agent, and the biasing is based at least in part on content provided to the local agent by a third-party (3P) agent that is in network communication with the local agent. In some of those implementations, the content includes contextual parameters that are provided by the 3P agent in combination with responsive content generated by the 3P agent during a dialog that: is between the 3P agent, and a user of a voice-enabled electronic device; and is facilitated by the local agent. The contextual parameters indicate potential feature(s) of further voice input that is to be provided in response to the responsive content generated by the 3P agent.

Abstract: Implementations relate to dynamically, and in a context-sensitive manner, biasing voice to text conversion. In some implementations, the biasing of voice to text conversions is performed by a voice to text engine of a local agent, and the biasing is based at least in part on content provided to the local agent by a third-party (3P) agent that is in network communication with the local agent. In some of those implementations, the content includes contextual parameters that are provided by the 3P agent in combination with responsive content generated by the 3P agent during a dialog that: is between the 3P agent, and a user of a voice-enabled electronic device; and is facilitated by the local agent. The contextual parameters indicate potential feature(s) of further voice input that is to be provided in response to the responsive content generated by the 3P agent.

Abstract: Implementations relate to dynamically, and in a context-sensitive manner, biasing voice to text conversion. In some implementations, the biasing of voice to text conversions is performed by a voice to text engine of a local agent, and the biasing is based at least in part on content provided to the local agent by a third-party (3P) agent that is in network communication with the local agent. In some of those implementations, the content includes contextual parameters that are provided by the 3P agent in combination with responsive content generated by the 3P agent during a dialog that: is between the 3P agent, and a user of a voice-enabled electronic device; and is facilitated by the local agent. The contextual parameters indicate potential feature(s) of further voice input that is to be provided in response to the responsive content generated by the 3P agent.

Abstract: Implementations relate to dynamically, and in a context-sensitive manner, biasing voice to text conversion. In some implementations, the biasing of voice to text conversions is performed by a voice to text engine of a local agent, and the biasing is based at least in part on content provided to the local agent by a third-party (3P) agent that is in network communication with the local agent. In some of those implementations, the content includes contextual parameters that are provided by the 3P agent in combination with responsive content generated by the 3P agent during a dialog that: is between the 3P agent, and a user of a voice-enabled electronic device; and is facilitated by the local agent. The contextual parameters indicate potential feature(s) of further voice input that is to be provided in response to the responsive content generated by the 3P agent.

Abstract: Implementations relate to dynamically, and in a context-sensitive manner, biasing voice to text conversion. In some implementations, the biasing of voice to text conversions is performed by a voice to text engine of a local agent, and the biasing is based at least in part on content provided to the local agent by a third-party (3P) agent that is in network communication with the local agent. In some of those implementations, the content includes contextual parameters that are provided by the 3P agent in combination with responsive content generated by the 3P agent during a dialog that: is between the 3P agent, and a user of a voice-enabled electronic device; and is facilitated by the local agent. The contextual parameters indicate potential feature(s) of further voice input that is to be provided in response to the responsive content generated by the 3P agent.

Abstract: Implementations relate to dynamically, and in a context-sensitive manner, biasing voice to text conversion. In some implementations, the biasing of voice to text conversions is performed by a voice to text engine of a local agent, and the biasing is based at least in part on content provided to the local agent by a third-party (3P) agent that is in network communication with the local agent. In some of those implementations, the content includes contextual parameters that are provided by the 3P agent in combination with responsive content generated by the 3P agent during a dialog that: is between the 3P agent, and a user of a voice-enabled electronic device; and is facilitated by the local agent. The contextual parameters indicate potential feature(s) of further voice input that is to be provided in response to the responsive content generated by the 3P agent.

Abstract: In one example, a method includes determining, by one or more processors of a computing system included in a vehicle, whether one or more events have occurred that are likely to increase a stress level of an operator of the vehicle, and determining, by the one or more processors, whether the operator is the only person in the vehicle. In this example, the method includes, responsive to determining that one or more events have occurred that are likely to increase the stress level of the operator of the vehicle and that the operator is the only person in the vehicle, outputting, by the one or more processors and for playback by one or more output devices included in the vehicle, media data likely to reduce the stress level of the operator.

Abstract: In one example, a method incudes determining, by one or more processors of a computing system included in a vehicle, whether one or more events have occurred that are likely to increase a stress level of an operator of the vehicle, and determining, by the one or more processors, whether the operator is the only person in the vehicle. In this example, the method includes, responsive to determining that one or more events have occurred that are likely to increase the stress level of the operator of the vehicle and that the operator is the only person in the vehicle, outputting, by the one or more processors and for playback by one or more output devices included in the vehicle, media data likely to reduce the stress level of the operator.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for augmenting visible content of ad creatives. In various implementations, a document associated with a destination linked to by an ad creative may be identified. One or more templates may be applied to content of the document to identify at least one content candidate with which to augment visible content of the ad creative. It may be determined that the at least one content candidate satisfies a criterion. Visible content of the ad creative may be augmented based on the at least one content candidate.

Abstract: Methods and apparatus related to automatically rewriting a string of text utilizing one or more rewrite rules. Some implementations are directed to scoring rewrite rules based at least in part on user interactions with rewrites that are generated by applying the rewrite rules. Some implementations are directed to determining the effectiveness of a rewrite generated based on applying one or more rewrite rules to a string of text. In some of those implementations, the determination may be based at least in part on one or more characteristics of the string of text, one or more characteristics of the rewrite, and/or scores associated with the rewrite rules.

Abstract: Methods and apparatus related to determining an edit rule based on a plurality of edits. Some implementations are directed to identifying the plurality of edits in one or more documents and determining an edit rule based on the pre-edit and post-edit phrases of the edits. Some implementations are directed to identifying the edits from one or more mature documents. The determined edit rule may be utilized to determine one or more candidate rephrasings of a subsequent phrase.

Abstract: Methods and apparatus related to determining an edit rule based on a plurality of edits. Some implementations are directed to identifying the plurality of edits in one or more documents and determining an edit rule based on the pre-edit and post-edit phrases of the edits. Some implementations are directed to identifying the edits from one or more mature documents. The determined edit rule may be utilized to determine one or more candidate rephrasings of a subsequent phrase.

Abstract: Methods and apparatus related to automatically rewriting a string of text utilizing one or more rewrite rules. Some implementations are directed to scoring rewrite rules based at least in part on user interactions with rewrites that are generated by applying the rewrite rules. Some implementations are directed to determining the effectiveness of a rewrite generated based on applying one or more rewrite rules to a string of text. In some of those implementations, the determination may be based at least in part on one or more characteristics of the string of text, one or more characteristics of the rewrite, and/or scores associated with the rewrite rules.