Abstract: A system includes a development system and a digital assistance system. The development system includes a network interface configured to communicate with a plurality of communication channels, a processing system configured to interface with a project management subsystem, a scheduling subsystem, and the network interface, and an application programming interface configured to receive a command sequence for the project management subsystem and the scheduling subsystem. The digital assistance system includes a natural language processing engine configured to interface with a voice-enabled communication session through one of the communication channels. The digital assistance system also includes a command generator configured to generate the command sequence based on one or more requested tasks detected through the voice-enabled communication session and provide the command sequence to the application programming interface to execute the one or more requested tasks.

Abstract: A filtering device is configured to estimate the characteristics of noise superposed on measurement data relating to the status of a controlled machine based on the status information representing the status of a controlled machine, thus adjusting the filtering to eliminate noise based on the estimated noise characteristics.

Abstract: A graphical user interface (GUI) and pipeline for processing text documents is provided herein. In one example, a system can receive unstructured text documents. The system can determine entity-issue descriptions corresponding to the unstructured text documents. The system can then generate a GUI indicating the entity-issue descriptions. The GUI can also indicate assignments of the unstructured text documents to categories of a predefined schema. The GUI can allow the user to adjust the assignments of the unstructured text documents to the categories. The GUI can also include a table of rows, where each row corresponds to one of the unstructured text documents. Each row can indicate an entity-issue description in the corresponding unstructured text document and the categories assigned to the unstructured text document. Each row can also include a graphical button that is selectable to allow the user to view the unstructured text document corresponding to the row.

Abstract: The present application provides a robot response method, apparatus, device and storage medium. The method includes: obtaining, by a robot, current query voice; extracting semantic information of the current query voice; matching the semantic information of the current query voice with multiple semantic information clusters stored in advance to get a matched target semantic information cluster, where each semantic information cluster includes: at least one Q&A instance, and each Q&A instance includes: semantic information corresponding to a historical query voice and a query question selected in a query list corresponding to the historical query voice; and obtaining, by the robot, the number of times each query question was selected in the target semantic information cluster, determining, according to the number of times each query question was selected, a target query question corresponding to the current query voice, and outputting a query response corresponding to the target query question.

Abstract: Systems and methods for distributed voice processing are disclosed herein. In one example, the method includes detecting sound via a microphone array of a first playback device and analyzing, via a first wake-word engine of the first playback device, the detected sound. The first playback device may transmit data associated with the detected sound to a second playback device over a local area network. A second wake-word engine of the second playback device may analyze the transmitted data associated with the detected sound. The method may further include identifying that the detected sound contains either a first wake word or a second wake word based on the analysis via the first and second wake-word engines, respectively. Based on the identification, sound data corresponding to the detected sound may be transmitted over a wide area network to a remote computing device associated with a particular voice assistant service.

Abstract: A method of electronic communication assistance is provided. The method includes receiving, via an artificial intelligence assistant computing facility, an electronic communication from a first user intended to be received by a second user; and determining, via the artificial intelligence assistant computing facility, a capacity of the second user to receive the electronic communication. The method further includes determining, via the artificial intelligence assistant computing facility and based at least in part on the capacity of the second user, a time to send the electronic communication; and transmitting, via the artificial intelligence assistant computing facility, the time to the first user.

Abstract: A computing device receives user selection of a data source. In accordance with the user selection, the device generates one or more initial natural language utterances according to metrics of data fields in the data source and/or previous user interaction with the data source. Each of the initial natural language utterances corresponds to a respective suggestion to guide visual analysis of the data source. The device displays the initial utterances in a graphical user interface. The device receives user selection of a first initial utterance of the initial utterances. In response to the user selection, the device generates and displays a first data visualization in accordance with data fields and/or analytical operations specified in the first initial utterance. The device also generates updated natural language utterances in accordance with the first initial utterance and the first data visualization, and displays the updated utterances with the first data visualization.

Abstract: The present invention relates to an AI-based voice ordering system and a method therefor and provides a voice ordering method and system, the voice ordering method comprising: a first step of an ordering smart terminal standing by for voice data reception; a second step of the ordering smart terminal analyzing whether an input signal has been received by an input unit corresponding to a microphone activation button; and a third step of, if the analysis result indicates that an input signal has not been received, returning to the first step and, conversely, if an input signal has been received, the ordering smart terminal receiving a voice signal from a microphone, converting the voice signal into voice data of a preset format, and then transmitting the converted voice data to a voice ordering server via a host terminal connected to a network, so that analysis of text data is performed.

Abstract: A system receives a speech of a user that indicates a request. The system extracts a plurality of voice features from the speech. The system converts the speech into a plurality of binary digits. The system determines a first voice feature constant value associated with a first voice feature, where the first voice feature constant value is an average of the first voice feature. The system determines a second voice feature constant value associated with the second voice feature, where the second voice feature constant value is an average of the second voice feature. The system encrypts the plurality of binary digits using the first and second voice feature constant values, where the encrypted plurality of binary digits corresponds to a voice-based hash value. The system generates a new block in a blockchain network using the voice-based hash value.

Abstract: Voice-based interaction with video content being presented by a media player application is enhanced through the use of an automated assistant capable of identifying when a spoken utterance by a user is a request to playback a specific scene in the video content. A query identified in a spoken utterance may be used to access stored scene metadata associated with video content being presented in the vicinity of the user to identify one or more locations in the video content that correspond to the query, such that a media control command may be issued to the media player application to cause the media player application to seek to a particular location in the video content that satisfies the query.

Abstract: A voice information processing apparatus sequentially converts an utterance of a user into text during a voice reception period that is a period in which an uttered voice to be converted into text is received from a user, and in a case where it can be regarded that the utterance of the user has been interrupted, the voice information processing apparatus automatically causes utterance content already uttered by the user to be output by a voice during the voice reception period. As a result, the voice information processing apparatus can cause the user to recognize a content of a sentence that has been uttered by the user so far and converted into text, when it can be regarded that the utterance of the user has been interrupted.

Abstract: Generating and/or recommending command bundles for a user of an automated assistant. A command bundle comprises a plurality of discrete actions that can be performed by an automated assistant. One or more of the actions of a command bundle can cause transmission of a corresponding command and/or other data to one or more devices and/or agents that are distinct from devices and/or agents to which data is transmitted based on other action(s) of the bundle. Implementations determine command bundles that are likely relevant to a user, and present those command bundles as suggestions to the user. In some of those implementations, a machine learning model is utilized to generate a user action embedding for the user, and a command bundle embedding for each of a plurality of command bundles. Command bundle(s) can be selected for suggestion based on comparison of the user action embedding and the command bundle embeddings.

Abstract: A virtual assistant server receives an utterance provided by an end user via a channel of a virtual assistant rendered in a client device. The virtual assistant server identifies a current-node of execution from a plurality of nodes of a conversation definition of the virtual assistant and identifies a first set of language models from a group of language models of the virtual assistant to interpret the utterance. Further, the virtual assistant server executes the first set of language models in an order based on the current-node until an intent of the utterance is determined. Subsequently, the virtual assistant server generates a response based on the intent and outputs the response to the client device.

Abstract: A method of training a custom hotword model includes receiving a first set of training audio samples. The method also includes generating, using a speech embedding model configured to receive the first set of training audio samples as input, a corresponding hotword embedding representative of a custom hotword for each training audio sample of the first set of training audio samples. The speech embedding model is pre-trained on a different set of training audio samples with a greater number of training audio samples than the first set of training audio samples. The method further includes training the custom hotword model to detect a presence of the custom hotword in audio data. The custom hotword model is configured to receive, as input, each corresponding hotword embedding and to classify, as output, each corresponding hotword embedding as corresponding to the custom hotword.

Abstract: Systems, devices, and methods are discussed relating to plasma sources using load current switch timing of zero volt switching resonant topology.

Abstract: A method and apparatus for training a speech recognition model, an electronic device and a storage medium are provided.

Abstract: A method for training one or more AI models for generating audio scores accompanying visual datasets includes obtaining training data comprising a plurality of audiovisual datasets and analyzing each of the plurality of audiovisual datasets to extract multiple visual features, textual features, and audio features. The method also includes correlating the multiple visual features and textual features with the multiple audio features via a machine learning network. Based on the correlations between the visual features, textual features, and audio features, one or more AI models are trained for composing one or more audio scores for accompanying a given dataset.

Abstract: An electronic device is disclosed. The electronic device comprises: a voice reception unit for receiving user's voice; a storage unit for storing a first speech recognition module for recognizing user's voice and a second speech recognition module for recognizing only predetermined voice in the user's voice; and a processor for performing speech recognition of only a part of the user's voice through the first speech recognition module, when a result of speech recognition through the second speech recognition module shows that the user's voice includes the predetermined voice.

Abstract: Systems and methods for detecting a convenient time to play an audible announcement. The techniques described herein can be implemented for audible announcements in various settings, including, for example, audible announcements in an autonomous vehicle and audible announcements from a mapping service on a mobile device. In an autonomous vehicle, interior microphones can be used to detect voices within the vehicle and identify pauses in conversation. Audible notifications and announcements within the autonomous vehicle can then be made during the pauses.

Abstract: Systems, methods, devices, and other techniques are described herein for determining dialog states that correspond to voice inputs and for biasing a language model based on the determined dialog states. In some implementations, a method includes receiving, at a computing system, audio data that indicates a voice input and determining a particular dialog state, from among a plurality of dialog states, which corresponds to the voice input. A set of n-grams can be identified that are associated with the particular dialog state that corresponds to the voice input. In response to identifying the set of n-grams that are associated with the particular dialog state that corresponds to the voice input, a language model can be biased by adjusting probability scores that the language model indicates for n-grams in the set of n-grams. The voice input can be transcribed using the adjusted language model.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating an output transcript from an input audio segment using a multi-task transformer model. In some embodiments, the transformer model can be trained to transcribe or translate audio data in multiple languages using labeled audio data. The labeled audio data can include first audio segments associated with first same-language transcripts of the first audio segments and second audio segments associated with second different-language transcripts of the second audio segments. In some embodiments, a vocabulary of the model can include special purpose and time stamp tokens. The special purpose tokens can specify tasks for the model to perform.

Abstract: 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.

Abstract: An electronic device with one or more processors and memory includes a procedure for enabling conversation persistence across two or more instances of a digital assistant. In some embodiments, the device displays a first dialogue in a first instance of a digital assistant user interface. In response to a request to display a user interface different from the digital assistant user interface, the device displays the user interface different from the digital assistant user interface. In response to a request to invoke the digital assistant, the device displays a second instance of the digital assistant user interface, including displaying a second dialogue in the second instance of the digital assistant user interface, where the first dialogue remains available for display in the second instance of the digital assistant user interface.

Abstract: Implementations described herein relate to providing suggestions, via a display modality, for completing a spoken utterance for an automated assistant, in order to reduce a frequency and/or a length of time that the user will participate in a current and/or subsequent dialog session with the automated assistant. A user request can be compiled from content of an ongoing spoken utterance and content of any selected suggestion elements. When a currently compiled portion of the user request (from content of a selected suggestion(s) and an incomplete spoken utterance) is capable of being performed via the automated assistant, any actions corresponding to the currently compiled portion of the user request can be performed via the automated assistant. Furthermore, any further content resulting from performance of the actions, along with any discernible context, can be used for providing further suggestions.

Abstract: The present disclosure provides a dialog generation method, performed by a human-machine dialog system. The method includes obtaining an input dialog sequence from a dialog client; obtaining associated information related to the input dialog sequence; encoding, by an encoder, the input dialog sequence to obtain an input encoding vector; encoding, by the encoder, the associated information to obtain an associated encoding vector; decoding, by a decoder, the input encoding vector and the associated encoding vector to obtain an output dialog sequence, the output dialog sequence comprising an out-of-vocabulary word corresponding to the associated information; and transmitting the output dialog sequence to the dialog client.

Abstract: In one example, a method for evaluating screen content for accessibility with a screen reader device is disclosed. The method provides a baseline document including a script of expected screen content that conforms accessibility requirements. The method may generate an audio file based on screen content elements. For some implementations, the method uses a machine learning model to transcribe the audio file into an output transcription file. The method may determine whether output transcription file matches the baseline document and a corresponding output report is generated.

Abstract: One embodiment provides a method, including: receiving, at an input device associated with an information handling device, audio input; determining, using a processor, that an audible anomaly exists in the audio input, wherein the audible anomaly corresponds to a deviation from an established speech input pattern of a user; and performing, responsive to determining that the audible anomaly exists in the audio input, a remedial action to address the audible anomaly. Other aspects are described and claimed.

Abstract: Media, methods, and systems of recommending personalized help content within a group-based communication system. A machine learning model trained with prior user interaction data and historical user engagement data is used to generate a list of recommended help content based at least in part on received user interaction data for a user.

Abstract: Methods and apparatus directed to utilizing an automated messaging system to initiate and/or adapt a dialog with at least one user, where the dialog occurs via user interface input and output devices of at least one computing device of the user. In some of those implementations, the automated messaging system identifies at least one task associated with the user and initiates the dialog with the user based on identifying the task. The automated messaging system may initiate the dialog to provide the user with additional information related to the task and/or to determine, based on user input provided during the dialog, values for one or more parameters of the task. In some implementations, the automated messaging system may further initiate performance of the task utilizing parameters determined during the dialog.

Abstract: Generating a compliance framework. The compliance framework facilitates an organization's compliance with multiple authority documents by providing efficient methodologies and refinements to existing technologies, such as providing hierarchical fidelity to the original authority document; separating auditable citations from their context (e.g., prepositions and or informational citations); asset focused citations; SNED and Live values, among others.

Abstract: The present disclosure provides methods and systems that may be used for providing quality control for audio samples. The audio samples may be speech samples of a user. The user may be participating in an audio interview.

Abstract: Devices and techniques are generally described for learning personalized responses to declarative natural language inputs. In various examples, a first natural language input may be received. The first natural language input may correspond to intent data corresponding to a declarative user input. In some examples, a dialog session may be initiated with the first user. An action intended by the first user for the first natural language input may be determined based at least in part on the dialog session. In various examples, first data representing the action may be stored in association with second data representing a state described by at least a portion of the first natural language input.

Abstract: An electronic apparatus includes a memory storing instructions, a plurality of chat-bots for responding to a plurality of questions, and a question classification model trained to identify a chat-bot among the plurality of chat-bots for responding to an input question, and a processor configured to execute the instructions to input the input question into the question classification model, the question classification model outputting a first chat-bot among the plurality of chat-bots for responding to the input question, acquire a first response for the input question, through the outputted first chat-bot, based on the acquired first response comprising information of a function that can be performed at the electronic apparatus, generate a question requesting to perform the function, and input the generated question into the question classification model, the question classification model outputting a second chat-bot among the plurality of chat-bots for responding to the generated question.

Abstract: An interactive system may be implemented in part by an audio device located within a user environment, which may accept speech commands from a user and may also interact with the user by means of generated speech. In order to improve performance of the interactive system, a user may use a separate device, such as a personal computer or mobile device, to access a graphical user interface that lists details of historical speech interactions. The graphical user interface may be configured to allow the user to provide feedback and/or corrections regarding the details of specific interactions.

Abstract: During a feature-selection technique, an electronic device calculates combinations of features and noise vectors, where a given combination corresponds to a given feature and a given noise vector. Then, the electronic device determines statistical associations between information specifying types of events and the combinations, where a given statistical association corresponds to the types of events and a given combination. Moreover, the electronic device identifies a noise threshold associated with the combinations. Next, for a group of combinations having statistical associations equal to or greater than the noise threshold, the electronic device selects a subset of the features based at least in part on a first aggregate property of the group of combinations, where the first aggregate property comprises numbers of occurrences of the features in the group of combinations.

Abstract: An electronic device according to various embodiments includes: a microphone, a memory, and a processor, wherein the memory stores instructions which, when executed, cause the processor to: control the electronic device to copy a signal acquired from the microphone, record a first signal, and perform voice recognition on a second signal based on a recording operation using the microphone being executed, determine an editing section including unnecessary information in the first signal based on a voice recognition result for the second signal, and generate recording data by editing the unnecessary information included in the determined editing section in the first signal.

Abstract: Presented herein are techniques for augmenting a speech recognition engine. According to the disclosed techniques, audio data is obtained as part of an automatic speech recognition session. Speech hints are also obtained as part of the automatic speech recognition session. A dynamic language model is generated from the speech hints for use during the automatic speech recognition session. A combined language model is then generated from the dynamic language model and a static language model. Finally, the audio data is converted to text using the combined language model as part of the automatic speech recognition session.

Abstract: Systems and methods for providing an online to offline service in response to a voice request from a user terminal are provided. A method includes: receiving a voice request from a user terminal; in response to the voice request, updating a customized recognition model trained using data of a plurality of points of interest associated with the user terminal; obtaining a general recognition model trained using data from general public; determining a literal destination associated with the voice request based at least on the voice request, the customized recognition model and the general recognition model.

Abstract: The present disclosure provides a speech interaction method, apparatus, device and computer storage medium and relates to the field of artificial intelligence. A specific implementation solution is as follows: performing speech recognition and demand analysis for a first speech instruction input by a user; performing demand prediction for the first speech instruction if the demand analysis fails, to obtain at least one demand expression; returning at least one of the demand expression to the user in a form of a question; performing a service response with a demand analysis result corresponding to the demand expression confirmed by the user, if a second speech instruction confirming at least one of the demand expression is received from the user. The present disclosure can efficiently improve the user's interaction efficiency and enhance the user's experience.

Abstract: Computer-based natural language understanding of input and output for a computer interlocutor is improved using a method of classifying conversation segments from transcribed conversations. The improvement includes one or more methods of splitting transcribed conversations into groups related to a conversation ontology using metadata; identifying dominant paths of conversational behavior by counting the frequency of occurrences of the behavior for a given path; creating a conversation model comprising conversation behaviors, metadata, and dominant paths; and using the conversation model to assign a probability score for a matched input to the computer interlocutor or a generated output from the computer interlocutor.

Abstract: Implementations set forth herein relate to an automated assistant that can be invoked while a user is interfacing with a foreground application in order to retrieve data from one or more different applications, and then provide the retrieved data to the foreground application. A user can invoke the automated assistant while operating the foreground application by providing a spoken utterance, and the automated assistant can select one or more other applications to query based on content of the spoken utterance. Application data collected by the automated assistant from the one or more other applications can then be used to provide an input to the foreground application. In this way, the user can bypass switching between applications in the foreground in order to retrieve data that has been generated by other applications.

Abstract: Apparatus and methods for leveraging machine learning and artificial intelligence to generate a response to an utterance expressed by a user during an interaction between an interactive response system and the user is provided. The methods may include a natural language processor processing the utterance to output an utterance intent. The methods may also include a signal extractor processing the utterance, the utterance intent and previous utterance data to output utterance signals. The methods may additionally include an utterance sentiment classifier using a hierarchy of rules to extract, from a database, a label, the extracting being based on the utterance signals. The methods may further include a sequential neural network classifier using a trained algorithm to process the label and a sequence of historical labels to output a sentiment score. The methods may further include, based on the utterance intent, the label and the score, to output a response.

Abstract: A method includes adding, by a first computing device, a first audio watermark to first speech data corresponding to playback of a first utterance including a hotword used to invoke an attention of a second computing device. The method includes outputting, by the first computing device, the playback of the first utterance corresponding to the watermarked first speech data. The second computing device is configured to receive the watermarked first speech data and determine to cease processing of the watermarked first speech data.

Abstract: A multi-tag concurrent identification method and a system for a query tree based on feature groups are provided in this disclosure. In the disclosure, a whole data string space is divided into a plurality of disjoint subsets according to features of data strings returned by tags, where each of the subsets contains several different data strings, each of the data strings in the each of the subsets is regarded as a complete tag ID or a partial ID, and the each of the subsets corresponds to a unique query prefix, a length of the prefix is fixed and does not dynamically increase with an actual location of a collision, and when multiple data strings from a same subset return at a same time, a reader is capable of identifying them at a same time in a slot.

Abstract: In one embodiment, a method includes receiving, at a client system, an audio input, where the audio input comprises a coreference to a target object, accessing visual data from one or more camera associated with the client system, where the visual data comprises images portraying one or more objects, resolving the coreference to the target object from among the one or more objects, resoling the target object to a specific entity, and providing, at the client system, a response to the audio input, where the response comprises information about the specific entity.

Abstract: An electronic apparatus including a memory configured to store first voice recognition information related to a first language and second voice recognition information related to a second language, and a processor to obtain a first text corresponding to a user voice that is received on the basis of first voice recognition information, based on an entity name being included in the user voice according to the obtained first text, identify a segment in the user voice in which the entity name is included. The processor is to obtain a second text corresponding to the identified segment of the user voice on the basis of the second voice recognition information, and obtain control information corresponding to the user voice on the basis of the first text and the second text.

Abstract: In one aspect, a networked microphone device is configured to (i) receive sound data, (ii) determine, via the wake-word engine, that a first portion of the sound data is representative of a wake word, (iii) determine that a second networked microphone device was added to a media playback system, (iv) transmit the first portion of the sound data to a second networked microphone device, (v) begin determining a command to be performed by the first networked microphone device, (vi) receive an indication of whether the first portion of the sound data is representative of the wake word, and (vii) output a response indicative of whether the first portion of the sound data is representative of the wake word.

Abstract: Techniques are described herein for detecting and/or enrolling (or commissioning) new “hot commands” that are useable to cause an automated assistant to perform responsive action(s) without having to be first explicitly invoked. In various implementations, an automated assistant may be transitioned from a limited listening state into a full speech recognition state in response to a trigger event. While in the full speech recognition state, the automated assistant may receive and perform speech recognition processing on a spoken command from a user to generate a textual command. The textual command may be determined to satisfy a frequency threshold in a corpus of textual commands Consequently, data indicative of the textual command may be enrolled as a hot command. Subsequent utterance of another textual command that is semantically consistent with the textual command may trigger performance of a responsive action by the automated assistant, without requiring explicit invocation.

Abstract: Techniques for performing multi-stage entity resolution (ER) processing are described. A system may determine a portion of a user input corresponding to an entity name, and may request an entity provider component to perform a search to determine one or more entities corresponding to the entity name. The preliminary search results may be sent to a skill selection component for processing, while the entity provider component performs a complete search to determine entities corresponding to the entity name. A selected skill component may request the complete search results to perform its processing, including determining an output responsive to the user input.

Abstract: Aspects of the disclosure relate to using an apparatus for flagging and removing real time workflows that produce sub-optimal results. Such an apparatus may include an utterance sentiment classifier. The apparatus stores a hierarchy of rules. Each of the rules is associated with one or more rule signals. In response to receiving the one or more utterance signals, the classifier iterates through the hierarchy of rules in sequential order to identify a first rule for which the one or more utterance signals are a superset of the rule's one or more rule signals. In response to receiving the one or more alternate utterance signals from the signal extractor, the classifier may iterate through the hierarchy of rules in sequential order to identify the first rule in the hierarchy for which the one or more alternate utterance signals are a superset of the first rule's one or more rule signals.