Abstract: An electronic apparatus is provided. The electronic apparatus includes a microphone, a memory configured to store a plurality of keyword recognition models, and a processor, which is coupled with the microphone and the memory, configured to control the electronic apparatus, wherein the processor is configured to selectively execute at least one keyword recognition model among the plurality of keyword recognition models based on operating state information of the electronic apparatus, based on a first user voice being input through the microphone, identify whether at least one keyword corresponding to the executed keyword recognition model is included in the first user voice by using the executed keyword recognition model, and based on at least one keyword identified as being included in the first user voice, perform an operation of the electronic apparatus corresponding to the at least one keyword.

Abstract: A computer-implemented method executed by at least one processor for performing mini-batching in deep learning by improving cache utilization is presented. The method includes temporally localizing a candidate clip in a video stream based on a natural language query, encoding a state, via a state processing module, into a joint visual and linguistic representation, feeding the joint visual and linguistic representation into a policy learning module, wherein the policy learning module employs a deep learning network to selectively extract features for select frames for video-text analysis and includes a fully connected linear layer and a long short-term memory (LSTM), outputting a value function from the LSTM, generating an action policy based on the encoded state, wherein the action policy is a probabilistic distribution over a plurality of possible actions given the encoded state, and rewarding policy actions that return clips matching the natural language query.

Abstract: Provided are a method and apparatus for classifying a sentence into a class by using a deep neural network. The method includes respectively training first and second sentences by using first and second neural networks, obtaining a contrastive loss based on first and second feature vectors generated as output data of the training, and information about whether classes to which the first and second sentences belong are the same, and repeating the training in such a manner that the contrastive loss has a maximum value.

Abstract: An embodiment of a spoken intent detection device includes technology to detect a phrase in an electronic representation of an audio stream based on a pre-defined vocabulary, associate a time stamp with the detected phrase, and classify a spoken intent based on a sequence of detected phrases and the respective associated time stamps. Other embodiments are disclosed and claimed.

Abstract: An electronic device and a method for controlling the same are provided. The electronic device includes a microphone, a memory storing at least one instruction and dialogue history information, and a processor configured to be connected to the microphone and the memory and control the electronic device, in which the processor, by executing the at least one instruction, is configured to, based on a user's voice being input via the microphone, obtain response information for generating a response sentence to the user's voice, select at least one template phrase for generating the response sentence to the user's voice based on the stored dialogue history information, generate the response sentence using the response information and the at least one template phrase, and output the generated response sentence.

Abstract: The system for responding to a set of words includes an interface and a processor. The interface is configured to receive the set of words. The processor is configured to determine a token from the set of words; determine an entity and an associated keyword from the token, wherein the entity is associated with an entity type; determine an intent based at least in part on the set of words; match the entity to an entity of the intent based on the associated keyword; and determine a response based on the intent and the entity.

Abstract: An electronic device is provided, which includes a user interface, at least one communication module, a microphone, at least one speaker, at least one processor operatively connected with the user interface, the at least one communication module, the microphone, and the at least one speaker, and at least one memory operatively connected with the at least one processor, wherein the at least one memory stores instructions, which when executed, instruct the at least one processor to while the electronic device is wiredly or wirelessly connected with an access point (AP) connected with at least one external electronic device, after receiving, through the microphone, part of a wake-up utterance to invoke a voice-based intelligent assistant service, broadcast identification information about the electronic device and receive identification information broadcast from the external electronic device, after receiving the whole wake-up utterance through the microphone, individually transmit first information related to

Abstract: Implementations set forth herein relate to an automated assistant that allows a user to create, edit, and/or share documents without directly interfacing with a document editing application. The user can provide an input to the automated assistant in order to cause the automated assistant to interface with the document editing application and create a document. In order to identify a particular action to perform with respect to a document, and/or identify a particular subsection within the document to direct the action, the automated assistant can rely on semantic annotations. As a user continues to interact with the automated assistant to edit a document, the semantic annotations can be updated according to how the document is changing and/or how the user refers to the document. This can allow the automated assistant to more readily fulfill document-related requests that may lack express details.

Abstract: According to some embodiments of the disclosure, a method is disclosed. The method includes receiving, by a processing device of an in-ear device, an audio signal from one or more microphones of the in-ear device. The method further includes extracting, by the processing device, one or more features of the audio signal and generating, by the processing device, an in-ear data object based on the one or more features. The method also includes publishing, by the processing device, the in-ear data object to an external system via a network.

Abstract: An electronic apparatus and a control method thereof are provided. The electronic apparatus includes a microphone, a camera, a memory storing an instruction, and a processor configured to control the electronic apparatus coupled with the microphone, the camera and the memory, and the processor is configured to, by executing the instruction, obtain a user image by photographing a user through the camera, obtain the user information based on the user image, and based on a user speech being input from the user through the microphone, recognize the user speech by using a speech recognition model corresponding to the user information among a plurality of speech recognition models.

Abstract: A computer implemented method includes receiving audio streams at a meeting server from two distributed devices that are streaming audio captured during an ad-hoc meeting between at least two users, comparing the received audio streams to determine that the received audio streams are representative of sound from the ad-hoc meeting, generating a meeting instance to process the audio streams in response to the comparing determining that the audio streams are representative of sound from the ad-hoc meeting, and processing the received audio streams to generate a transcript of the ad-hoc meeting.

Abstract: Data is received that encapsulates a spoken response to a prompt text comprising a string of words. Thereafter, the received data is transcribed into a string of words. The string of words is then compared with a prompt so that a similarity grid representation of the comparison can be generated that characterizes a level of similarity between the string of words in the spoken response and the string of words in the prompt text. The grid representation is then scored using at least one machine learning model. The score indicates a likelihood of the spoken response having been off-topic. Data providing the encapsulated score can then be provided. Related apparatus, systems, techniques and articles are also described.

Abstract: A system is provided for handling errors during automatic speech recognition by leveraging past inputs spoken by the user. The system may process a user input to determine an ASR hypothesis. The system may then determine an alternate representation of the user input based on the inputs provided by the user in the past, and whether the ASR hypothesis sufficiently matches one of the past inputs.

Abstract: Disclosed are an information processing method and apparatus capable of controlling an electronic device in a 5G communication environment by processing information based on execution of a mounted artificial intelligence (AI) algorithm or machine learning algorithm.

Abstract: Disclosed are a method and apparatus for remotely controlling an imaging apparatus. A method of controlling a remote control apparatus includes converting a spoken utterance of a user into an utterance text or receiving the utterance text, applying a generative model-based first learning model to the utterance text and generating an image having attributes corresponding to a context of the utterance text, and externally transmitting the image and the utterance text. In addition, a method of controlling an imaging apparatus includes receiving a first input including text or speech data and a second input including a first image, capturing at least one second image based on the first input, comparing the first image and the second image, and transmitting the second image in response to a comparison result of the first image and the second image.

Abstract: A method for facilitating user support using multimodal information involves obtaining an interaction between a user and a support agent, generating a question embedding from the interaction, obtaining a clickstream associated with the interaction, and generating a clickstream embedding from the clickstream. The question embedding and the clickstream embedding form a shared latent space representation. The method further involves decoding a problem summary from the shared latent space representation and providing the problem summary to the support agent.

Abstract: Systems, methods, and computer readable media comprising instructions executable by a processor, for recognizing speech within a received audio signal segment the audio signal to isolate the speech based on a speaker audio profile, determine from the audio signal a command, a first score reflecting confidence in determining the command, and a second score reflecting a potential error in determining the command, and cause the command to be executed if the first score is above a first threshold value and the second score is below a second threshold value.

Abstract: A hub-dialogue system includes a speech processor configured to determine a user intention that is included in inputted user dialogue. A communicator is configured to connect a local speaker corresponding to an external dialogue system through wireless communication. A controller is configured to determine the external dialogue system corresponding to the user intention from among a plurality of external dialogue systems, to generate a command sentence indicating the user intention, to convert the command sentence to a voice signal, and to control the communicator to transmit the voice signal to the local speaker corresponding to the determined external dialogue system.

Abstract: An electronic device is provided.

Abstract: A language proficiency analyzer automatically evaluates a person's language proficiency by analyzing that person's oral communications with another person. The analyzer first enhances the quality of an audio recording of a conversation between the two people using a neural network that automatically detects loss features in the audio and adds those loss features back into the audio. The analyzer then performs a textual and audio analysis on the improved audio. Through textual analysis, the analyzer uses a multi-attention network to determine how focused one person is on the other and how pleased one person is with the other. Through audio analysis, the analyzer uses a neural network to determine how well one person pronounced words during the conversation.