Abstract: A learning device includes a memory, and processing circuitry coupled to the memory and configured to receive an input of a plurality of series for learning having known accuracy, and learn a model represented by a neural network, the model being capable of determining accuracy levels of two series when given feature amounts of the two series among the plurality of series.

Abstract: System and method of generating an executable action item in response to natural language dialogue are disclosed herein. A computing system receives a dialogue message from a remote client device of a customer associated with an organization, the dialogue message comprising an utterance indicative of an implied goal. A natural language processor of the computing system parses the dialogue message to identify one or more components contained in the utterance. The planning module of the computing system identifies the implied goal. The computing system generates a plan within a defined solution space. The computing system generates a verification message to the user to confirm the plan. The computing system transmits the verification message to the remote client device of the customer. The computing system updates an event queue with instructions to execute the action item according to the generated plan upon receiving a confirmation message from the remote client device.

Abstract: A method, computer program product, and computing system for defining a communication computing system within a computing network, wherein the computing network includes a plurality of disparate platforms configured to provide information concerning various topics; enabling a user to issue a verbal command concerning one or more of the plurality of disparate platforms; processing the verbal command to generate a platform-useable command based, at least in part, upon the verbal command; and providing the platform-useable command to at least a portion of the plurality of disparate platforms via the communication computing system.

Abstract: Embodiments provide a voice interaction method and an apparatus, and relate to the field of terminal technologies. Common voice skill commands in a first application scenario may be determined based on the first application scenario and a historical voice skill usage record, and displayed in a display interface. This can implement scenario-based recommendation of voice skill commands, to cover as many application scenarios as possible. In this application, after being woken up, the voice assistant determines the first application scenario based on one or more information items. The voice assistant determines the common voice skill commands in the first application scenario based on the first application scenario and the historical voice skill usage record. The voice assistant displays the common voice skill commands in the first application scenario in the display interface.

Abstract: In one example, a method being performed by a computer system comprises: receiving an image file containing a pathology report; performing an image recognition operation on the image file to extract input text strings; detecting, using a natural language processing (NLP) model, entities from the input text strings, each entity including a label and a value; extracting, using the NLP model, the values of the entities from the input text strings; converting, based on a mapping table that maps entities and values to pre-determined terminologies, the values of at least some of the entities to the corresponding pre-determined terminologies; and generating a post-processed pathology report including the entities detected from the input text strings and the corresponding pre-determined terminologies.

Abstract: A computer device acquires speech content. The device performs feature extraction on the speech content to obtain an intermediate feature. The intermediate feature is used for indicating an audio expression characteristic of the speech content. The device decodes the intermediate feature based on an attention mechanism to obtain a first word graph network. The device performs feature mapping on the intermediate feature based on pronunciation of the speech content to obtain a second word graph network. The device determines a recognition result of the speech content according to candidate word connection relationships indicated by the first word graph network and the second word graph network.

Abstract: A system and method for machine learning classification of user sentiment is disclosed. The method includes storing including a plurality of category information. The plurality of category information includes a set of domain-specific category information. The method further includes extracting a plurality of aspects from textual data. The method further includes generating a sentiment by a machine learning model. The method further includes receiving the plurality of aspects and the set of domain-specific category information. The method further includes generating a sentiment based on the plurality of aspects and the set of domain-specific category information.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for pre-processing dual-channel voice data for an automatic speech recognition mode. The method comprises creating one or more spectrograms for each channel of the dual-channel voice data by applying fast Fourier transform and generating power spectral density. The one or more balanced power spectrograms are created by merging the spectrograms of the channels, and are provided as input for acoustic and language processing by an automatic speech recognition machine learning model.

Abstract: According to an embodiment, an electronic device is provided. The electronic device includes: at least one processor; and a memory comprising instructions, which when executed, control the at least one processor to: receive a voice instruction of a user at the electronic device; transmit information regarding the voice instruction to a control device for identifying the user by mapping to a first voiceprint which is registered by another electronic device, a second voiceprint of the voice instruction of the user based on a voiceprint mapping model; and perform an operation corresponding to the voice instruction upon the identification of the user.

Abstract: Systems and methods are presented for recognizing and responding to voice commands at a local system and selectively streaming audio to a network-based computing system to recognize voice commands when the user provides a specific voice command to stream to the network-based computing system and/or when the user provides a voice command that is not recognizable by the local system.

Abstract: Described are methods and systems are for generating dynamic conversational queries. For example, as opposed to being a simply reactive system, the methods and systems herein provide means for actively determining a user's intent and generating a dynamic query based on the determined user intent. Moreover, these methods and systems generate these queries in a conversational environment.

Abstract: Disclosed is a full-duplex voice dialogue method applied to a voice dialogue terminal and including recording and uploading by an awakened voice dialogue terminal audio to a cloud server for determining a reply content and a first duration of the audio analyzed for determining the reply content; receiving by the voice dialogue terminal the reply content and the first duration sent by the cloud server; determining whether the first duration is equal to a duration from the moment awakening the voice dialogue terminal to the current moment of uploading the audio; and presenting the reply content to a user if consistent. Both the reply content determined by the cloud server and the duration of the audio is acquired, and the reply content is presented to the user only when the first duration and the second duration are determined as consistent, thereby ensuring proper reply content.

Abstract: Technical solutions relate to the fields of artificial intelligence technologies and voice technologies. A technical solution includes: performing voice recognition and demand analysis on a voice instruction input by a user; in response to an unknown demand obtained by the demand analysis, acquiring information of a query entity and query content using a result of the demand analysis, and acquiring reply information corresponding to the query content by communication with the query entity; and returning a first voice response to the user using the reply information.

Abstract: The technology relates to methods for detecting and classifying emotions in textual communication, and using this information to suggest graphical indicia such as emoji, stickers or GIFs to a user. Two main types of models are fully supervised models and few-shot models. In addition to fully supervised and few-shot models, other types of models focusing on the back-end (server) side or client (on-device) side may also be employed. Server-side models are larger-scale models that can enable higher degrees of accuracy, such as for use cases where models can be hosted on cloud servers where computational and storage resources are relatively abundant. On-device models are smaller-scale models, which enable use on resource-constrained devices such as mobile phones, smart watches or other wearables (e.g., head mounted displays), in-home devices, embedded devices, etc.

Abstract: An automatic speech sensitivity adjustment feature is provided. The described sensitivity feature can enable an automatic system adjustment of a sensitivity level based on the number and type of determined speech errors. The sensitivity level determines how sensitive the sensitivity feature will be when indicating speech errors. The sensitivity feature can receive audio input comprising one or more spoken words and determine speech errors for the audio input using at least a sensitivity level. The sensitivity feature can determine whether an amount and type of the speech errors requires an adjustment to the sensitivity level. The sensitivity feature can adjust the sensitivity level to a second sensitivity level based on the amount and type of the speech errors, where the second sensitivity level is a different level than the sensitivity level. The sensitivity feature can re-determine the speech errors for the audio input using at least the second sensitivity level.

Abstract: A computer-implemented method, computer system, and computer program product for measuring a quality of a chatbot response. The present invention may include receiving one or more classifications, receiving a set of questions in a chatbot to be analyzed, filtering any question from the received set of questions that is not related to an area of expertise of the chatbot, matching at least two questions from the received set of questions to each other, and applying at least one of the one or more classifications to the at least two matched questions. The one or more classifications may be based on a similarity of words and synonyms used in the at least two matched questions. The one or more classifications may be based on a similarity of intents of the at least two matched questions.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for transferring dialog sessions between devices using deep links. The dialog sessions can correspond to interactions, mediated by an automated assistant, between a user and a third party application. During the dialog session, a user can request that the dialog session be transferred to a different device, for example, to interact with the third party application through a different modality. In response, the automated assistant and/or the third party application can generate a link that can be transferred to the transferee device to allow the transferee device to seamlessly take over the dialog session. In this way, computational resources and electrical power can be preserved by not requiring a recipient device to re-process natural language inputs previously provided during the dialog session.

Abstract: A display device according to an embodiment of the present disclosure includes an output unit, a communication unit configured to perform communication with an artificial intelligence server, and a control unit configured to receive a voice command, convert the received voice command into text data, determine whether the converted text data is composed of a plurality of languages, when the text data is composed of the plurality of languages, determine a language for a voice recognition service among the plurality of languages based on the text data, and output an intent analysis result of the voice command in the determined language.

Abstract: A method provided for cross-lingual transfer trains a pre-trained multi-lingual language model based on a gold labeled training set in a source language to obtain a trained model. The method assigns each sample in an unlabeled target language set to a silver label according to a model prediction by the trained model to obtain set of silver labels, and performs uncertainty-aware label selection based on the silver label assigned to each sample according to the model prediction and the trained model to obtain selected silver labels. The method performs iterative training on the selected labels by applying the selected silver labels in the target language set as training labels and re-training the trained model with the gold labels and the selected silver labels to obtain an iterative model, and performs task-specific result prediction in target languages based on the iterative model to generate a final predicted result in target languages.

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.