Abstract: Implementations include identifying, from a database of entries reflecting past automated assistant commands submitted within a threshold amount of time relative to a current time, particular entries that each reflect corresponding features of a corresponding user submission of a particular command. Further, those implementations include determining that the particular command is a golden command, for a particular automated assistant function, responsive to determining that: at least a threshold percentage of the user submissions of the particular command triggered the particular automated assistant function, and a quantity of the user submission of the particular command satisfies a threshold quantity.

Abstract: Systems and methods are disclosed for creating a machine generated avatar. A machine generated avatar is an avatar generated by processing video and audio information extracted from a recording of a human speaking a reading corpora and enabling the created avatar to be able to say an unlimited number of utterances, i.e., utterances that were not recorded. The video and audio processing consists of the use of machine learning algorithms that may create predictive models based upon pixel, semantic, phonetic, intonation, and wavelets.

Abstract: Provided herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for enabling Do Not Disturb functionality in voice responsive devices. An example embodiment operates by: enabling an user to configure Do Not Disturb settings for a voice responsive device; while (a) the Do Not Disturb functionality is activated for the voice responsive device, and (b) within a Do Not Disturb time period specified by the Do Not Disturb settings: disabling one or more microphones; receiving an unambiguous trigger; responsive to receiving the unambiguous trigger, enabling the microphone(s); receiving a voice command; and processing the voice command. An example of an unambiguous trigger may be the user pressing a talk button (either a physical or digital button) on a remote control associated with the voice responsive device.

Abstract: A first electronic device of a local group of connected electronic devices receives a first voice command including a request for a first operation, assigns a first target device from among a local group of connected electronic devices as an in-focus device for performing the first operation, causes the first operation to be performed by the first target device via operation of a server-implemented common network service, receives a second voice command including a request for a second operation, and based on a determination that the second voice command does not include an explicit designation of a second target device and a determination that the second operation can be performed by the first target device, assigning the first target device as the in-focus device for performing the second operation.

Abstract: A speech recognition system and method thereof are provided. The speech recognition system connects to an external general-purpose speech recognition system, and including a storage unit and a processing unit. The storage unit stores a specific application speech recognition module, a comparison module and an enhancement module. The specific application speech recognition module converts a speech signal into a first phonetic text. The general-purpose speech recognition system converts the speech signal into a written text. The comparison module receives the first phonetic text and the written text, converts the written text into a second phonetic text, and aligns the second phonetic text with the first phonetic text according to similarity of pronunciation to output a phonetic text alignment result. The enhancement module receives the phonetic text alignment result, and constructs with the written text and the first phonetic text after path weighting to form an outputting recognized text.

Abstract: Methods and systems for correcting, based on subsequent second speech, an error in an input generated from first speech using automatic speech recognition, without an explicit indication in the second speech that a user intended to correct the input with the second speech, include determining that a time difference between when search results in response to the input were displayed and when the second speech was received is less than a threshold time, and based on the determination, correcting the input based on the second speech. The methods and systems also include determining that a difference in acceleration of a user input device, used to input the first speech and second speech, between when the search results in response to the input were displayed and when the second speech was received is less than a threshold acceleration, and based on the determination, correcting the input based on the second speech.

Abstract: A speech recognition apparatus and an operating method thereof which execute a mounted artificial intelligence (AI) algorithm and/or machine learning algorithm to perform speech recognition and communicate with different electronic apparatuses and external servers in a 5G communication environment are disclosed.

Abstract: Systems, methods, and devices for computer-generating responses and sending responses to communications when the recipient of the communication is unavailable are disclosed. An individual may send a message (either audio or text) to a recipient. The recipient may be unavailable to contemporaneously respond to the message (e.g., the recipient may be performing an action that makes is difficult or impractical for the recipient to contemporaneously respond to the audio message). When the recipient is unavailable, a response to the message is generated and sent without receiving an instruction from the recipient to do so. The response may be sent to the message originating individual, and content of the response may thereafter be sent to the recipient to receive feedback regarding the correctness of the response. Alternatively, the response content may first be sent to the recipient to receive the feedback, and thereafter the response may be sent to the message originating individual.

Abstract: Methods, apparatus, systems, and computer-readable media for engaging an automated assistant to perform multiple tasks through a multitask command. The multitask command can be a command that, when provided by a user, causes the automated assistant to invoke multiple different agent modules for performing tasks to complete the multitask command. During execution of the multitask command, a user can provide input that can be used by one or more agent modules to perform their respective tasks. Furthermore, feedback from one or more agent modules can be used by the automated assistant to dynamically alter tasks in order to more effectively use resources available during completion of the multitask command.

Abstract: A communication robot for operating a massage providing apparatus by executing an artificial intelligence algorithm (AI) and/or machine learning algorithm in a 5G environment connected for the Internet of things and a method for operating the communication robot are provided. The method for operating the communication robot may include acquiring a spoken utterance of a user positioned within a prescribed range from the massage providing apparatus, tracking a spoken utterance direction of the user from the spoken utterance of the user, orienting the communication robot towards the spoken utterance direction of the user, acquiring a voice command included in the spoken utterance in relation to operation of the massage providing apparatus, and operating the massage providing apparatus according to the voice command.

Abstract: An example computing system that maintains a playlist of media items for playback by one or more playback devices receives, from a first playback device, a request to play back the playlist and first operational data and also receives, from a second playback device, a request to play back the playlist and second operational data. The computing system provides the media items in the playlist to the first and second playback devices for substantially synchronous playback and, based on the first and second operational data, determines respective first and second advertisements. The computing system provides the first advertisement to the first playback device for playback during a given time slot designated for advertisements, and also provides the second advertisement to the second playback device for playback during the given time slot, such that the second playback device plays the second advertisement while the first playback device plays the first advertisement.

Abstract: Implementations set forth herein relate to an automated assistant that provides a response for certain user queries based on a level of interaction of the user with respect to the automated assistant. Interaction can be characterized by sensor data, which can be processed using one or more trained machine learning models in order to identify parameters for generating a response. In this way, the response can be limited to preserve computational resources and/or ensure that the response is more readily understood given the amount of interaction exhibited by the user. In some instances, a response that embodies information that is supplemental, to an otherwise suitable response, can be provided when a user is exhibiting a particular level of interaction. In other instances, such supplemental information can be withheld when the user is not exhibiting that particular level of interaction, at least in order to preserve computational resources.

Abstract: The present disclosure provides a speech skill creating method and system, wherein the method comprises: providing a speech skill creating interface in response to a developer's speech skill creating instruction; obtaining basic information and content configuration of the speech skill through the speech skill creating interface; in response to the developer's online publication instruction, adding a corresponding speech interaction capability for the content of the speech skill, and creating and publishing the speech skill. It is possible to, by employing the solutions of the present disclosure, complete the creation of the speech skill without performing any programming, and improve the development efficiency of the speech skill.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for utilizing multiple AI service providers by a dialog management system. The dialog management system can include a dispatcher bot, multiple worker bots, and multiple AI adapters that are each associated with a different cloud-based AI service provider. In response to receiving a query, the dispatcher bot selects a particular worker bot to handle the query. The particular worker bot is assigned to a particular AI service provider. An AI adapter associated with the particular AI service provider, generates a query message based on the query. The AI adapter sends the query message to the particular AI service provider and receives a response message. The dialog management system sends a representation of the response message to the particular worker bot, receives an answer for the query from the particular worker bot, and provides the answer for output.

Abstract: An electronic device is provided. The electronic device includes a memory configured to store a computer executable instructions; and a processor configured to execute the executable instructions to: determine a text corresponding to a received command, provide response information on the command based on a first artificial intelligence model classifying the text as a text corresponding to one of a plurality of pre-stored texts, and provide error information on the command based on the first artificial intelligence model classifying the text as an error, wherein the first artificial intelligence model is configured to classify the text as the error based on the text corresponding to the command being a similar text having one of an entity and an intent different from at least one of the plurality of pre-stored texts.

Abstract: A system configured to improve wakeword detection. The system may selectively rectify (e.g., attenuate) a portion of an audio signal based on energy statistics corresponding to a keyword (e.g., wakeword). For example, a device may perform echo cancellation to generate isolated audio data, may use the energy statistics to calculate signal quality metric values for a plurality of frequency bands of the isolated audio data, and may select a fixed number of frequency bands (e.g., 5-10%) associated with lowest signal quality metric values. To detect a specific keyword, the system determines a threshold ?(f) corresponding to an expected energy value at each frequency band. During runtime, the device determines signal quality metric values by subtracting residual music from the expected energy values. Thus, the device attenuates only a portion of the total number of frequency bands that include more energy than expected based on the energy statistics of the wakeword.

Abstract: A system is capable of automatically adjusting or reconstructing a baseline expression to generate a parallelized expression. Evaluation of the parallelized expression provide a substantially similar output as the evaluation of the baseline query in more efficient manner. In some implementations, data indicating an expression to be evaluated on a primary thread of the one or more processors is obtained. Elements of the expression are identified. The elements are grouped into a parse tree representation. Elements of the expression are classified as belonging to either a first category that includes elements that are eligible for parallel processing or a second category that includes elements that are not eligible for parallel processing. A particular element that is classified as belonging to the first category is identified and evaluated on a non-primary thread of the one or more processors. The non-primary thread is evaluated in parallel with the primary thread.

Abstract: Tracking social collaboration messages includes setting, by a computer, for a discussion group, a respective quota for each of a plurality of sentiment types assignable to a textual message; monitoring, by the computer, typing, by a sender, of text of a message for the discussion group; based on the monitored typing, determining, by the computer, a sentiment type of the message, the determined sentiment type being one of the plurality of sentiment types; and providing, by the computer, to the sender, recommendation for editing the message based on the quota set for the determined sentiment type.

Abstract: In some examples, a software agent executing on a server an utterance from a customer. The software agent converts the utterance to text. The software agent creates an audio representation of the text and performs a comparison of the audio representation and the utterance. The software agent creates edited text based on the comparison. For example, the software agent may determine, based on the comparison, audio differences between the audio representation and the utterance, create a sequence of edit actions based on the audio differences, and apply the sequence of edit actions to the text to create the edited text. The software agent outputs the edited text as a dialog response to the utterance.

Abstract: The present disclosure provides methods and devices for registering a voiceprint and authenticating a voiceprint. The method for registering a voiceprint includes performing a frame alignment operation on a registration character string inputted by a user in voice to extract first acoustic features of each first character constituting the registration character string; calculating a first posterior probability of the first acoustic features of each first character in a global Gaussian Mixture Model (GMM) model to perform a Baum-Welch (BW) statistic; extracting first vector features of each first character through a preset vector feature extractor configured for multi-character; and stitching the first vector features of each first character sequentially, to obtain a registration voiceprint model of the user.