Some implementations are directed to selective invocation of a particular third-party (3P) agent by an automated assistant to achieve an intended action determined by the automated assistant during a dynamic dialog between the automated assistant and a user. In some of those implementations, the particular 3P agent is invoked with value(s) for parameter(s) that are determined during the dynamic dialog; and/or the particular 3P agent is selected, from a plurality of candidate 3P agents, for invocation based on the determined value(s) for the parameter(s) and/or based on other criteria. In some of those implementations, the automated assistant invokes the particular 3P agent by transmitting, to the particular 3P agent, a 3P invocation request that includes the determined value(s) for the parameter(s).

A speech recognition method includes segmenting captured voice information to obtain a plurality of voice segments, and extracting voiceprint information of the voice segments; matching the voiceprint information of the voice segments with a first stored voiceprint information to determine a set of filtered voice segments having voiceprint information that successfully matches the first stored voiceprint information; combining the set of filtered voice segments to obtain combined voice information, and determining combined semantic information of the combined voice information; and using the combined semantic information as a speech recognition result when the combined semantic information satisfies a preset rule.

The present disclosure is generally related to a data processing system to selectively invoke applications for execution. A data processing system can receive an input audio signal and can parse the input audio signal to identify a command. The data processing system can identify a first functionality of a first digital assistant application hosted on the data processing system in the vehicle and a second functionality of a second digital assistant application accessible via a client device. The data processing system can determine that one of the first functionality or the second functionality supports the command. The data processing system can select one of the first digital assistant application or the second digital assistant application based on the determination. The data processing system invoke one of the first digital assistant application or the second digital assistant application based on the selection.

A vehicle control system executing a voice control system for facilitating voice-based dialog with a driver to enable the driver or autonomous vehicle to control certain operational aspects of an autonomous vehicle is provided. Using environmental and sensor input, the vehicle control system can select optimal routes for operating the vehicle in an autonomous mode or choose a preferred operational mode. Occupants of the autonomous vehicle can change a destination, route or driving mode by engaging with the vehicle control system in a dialog enabled by the voice control system.

A system of multi-modal transmission of packetized data in a voice activated data packet based computer network environment is provided. A natural language processor component can parse an input audio signal to identify a request and a trigger keyword. Based on the input audio signal, a direct action application programming interface can generate a first action data structure, and a content selector component can select a content item. An interface management component can identify first and second candidate interfaces, and respective resource utilization values. The interface management component can select, based on the resource utilization values, the first candidate interface to present the content item. The interface management component can provide the first action data structure to the client computing device for rendering as audio output, and can transmit the content item converted for a first modality to deliver the content item for rendering from the selected interface.

A spoken language understanding (SLU) system may include an automatic speech recognizer (ASR), an audio feature extractor, an optional synchronizer and a language understanding module. The ASR may produce a first set of input data representing transcripts of utterances. The audio feature extractor may produce a second set of input data representing audio features of the utterances, in particular, non-transcript specific characteristics of the speaker in one or more portions the utterances. The two sets of input data may be provided for the language understanding module to predict intents and slot labels for the utterances. The SLU system may use the optional synchronizer to align the two sets of input data before providing them to the language understanding module.

An operation method of a dialog agent includes obtaining an utterance history including at least one of an outgoing utterance to be transmitted to request a service or at least one of an incoming utterance to be received to request the service, updating a requirement specification including items requested for the service based on the utterance history, generating utterance information to be used to request the service based on the updated requirement specification, and outputting the generated utterance information.

A device monitors a communication between a user associated with a user device and a service representative associated with a service representative device, and causes a natural language processing model to perform a natural language processing analysis of a user input of the communication to identify a topic associated with the communication. The device determines a first score associated with the topic, and determines a second score associated with enabling the communication, where the first score and second score indicate a service performance score of an entity. The device causes a sentiment analysis model to perform a sentiment analysis of the communication to determine a sentiment score indicating a level of satisfaction the user has relative to the topic. The device updates a transaction protocol associated with the topic based on the service performance score, and/or updates a communication processing protocol associated with the communication based on the sentiment score.

A method to adaptively predict non-default actions against unstructured utterances by an automated assistant operating in a computing-system is provided. The method includes extracting voice-features based on receiving an input utterance from at-least one speaker by an automatic speech recognition (ASR) device, identifying the input utterance as an unstructured utterance based on the extracted voice-features and a mapping between the input utterance with one or more default actions as drawn by the ASR, obtaining at least one probable action to be performed in response to the unstructured utterance through a dynamic bayesian network (DBN). The method further includes providing the at least one probable action obtained by the DBN to the speaker in an order of the posterior probability with respect to each action.

A speech processing system uses contextual data to determine the specific domains, subdomains, and applications appropriate for taking action in response to spoken commands and other utterances. Some applications may be given priority over others such that some applications are general request applications to which responsibility for processing an intent is to be assigned as long as contextual criteria are satisfied, while other applications are specific request applications to which responsibility for processing an intent is to be assigned only if the applications are specifically requested, if the contextual criteria of priority applications are not satisfied, and/or if certain contextual criteria associated with the specific request applications are satisfied.