Abstract: Provided is a voice assistance system with proactive routines that couples a remote server and respective user voice interactive devices to deliver a complete experience to the end user of the device. The user devices can be managed by groups and/or associated entities who manage voice services for their users. For example, the entities can provide pre-configured voice routines that perform actions on behalf of their users. The voice assistance system can also allow users to customize these routines to improve day to day operation. In addition, external services and/or providers can be linked to the system and allowed to define routines that have external system dependencies. Avoiding and managing conflicts in this environment becomes quite challenging. Some approaches use execution queues and priority, others invoke time slices and limitations on assignment of routines to time slices to resolve these issues, among other examples.

Abstract: A voice command system according to a first disclosure comprises a gateway apparatus having an interface configured to receive a voice command, and a controller configured to perform a registration process of registering a speaker permitted to receive the voice command. The controller is configured to perform an authentication process of rejecting a reception of the voice command when a speaker of the voice command is not registered, and permitting a reception of the voice command when a speaker of the voice command is registered. The controller is configured to perform the authentication process for each voice command.

Abstract: A device and a method that determine an utterance type of a user utterance and generate a system response according to a determination result are achieved. A user utterance type determination unit that determines an utterance type of a user utterance, and a system response generation unit that generates a system response according to a type determination result determined by the user utterance type determination unit are included. The user utterance type determination unit determines whether the user utterance is of type A that requests all reutterances of a system utterance immediately before the user utterance, or type B that requests a reutterance of a part of the system utterance immediately before the user utterance.

Abstract: Systems and methods are provided herein for avoiding inadvertently trigging a voice assistant with audio played through a speaker. An audio signal is captured by sampling a microphone of the voice assistant at a sampling frequency that is higher than an expected finite sampling frequency of previously recorded audio played through the speaker to generate a voice data sample. A quality metric of the generated voice data sample is calculated by determining whether the generated voice data sample comprises artifacts resulting from previous compression or approximation by the expected finite sampling frequency. Based on the calculated quality metric, it is determined whether the captured audio signal is previously recorded audio played through the speaker. Responsive to the determination that the captured audio signal is previously recorded audio played through the speaker, the voice assistant refrains from being activated.

Abstract: Methods for generating and utilizing a multi-modal discourse tree (MMDT) are provided herein. An extended discourse tree (EDT) may be generated (e.g., from a discourse tree (DT) or a communicative DT (CDT)) from a corpus of text. Data records (e.g., records contained numerical data) may be linked to the extended discourse tree to generate a multi-modal discourse tree. The multi-modal discourse tree may link any suitable text/records from disparate sources. For example, entities identified from elementary discourse units of the EDT may be matched to an entity of a data record. Causal links may be identified between EDTs and/or data records. Rhetorical relationships can be identified for each entity/causal link match to incorporate the data records with the EDT to generate a MMDT. The MMDT may be used to classify subsequent input, to generate answers to subsequent questions, to navigate the corpus of text and/or data records, or the like.

Abstract: A non-transitory computer readable medium includes instructions that, when executed by at least one processor, cause the at least one processor to perform artificial-intelligence-based technical support operations.

Abstract: This invention relates to a computer implemented method (10) for processing a video file, said video file comprising audio content and visual content, the visual content comprising text content, wherein the method comprises: (S11) extracting the text content in the visual content; (S12) generating a context information for the audio content based on the text content extracted from said visual content; and (S13) converting the audio content into text by using the context information generated based on the text content extracted from the visual content of the video file.

Abstract: A method including transcribing, automatically, an ongoing stream of voice data into text phrases. The method also includes receiving an indication of a selected text phrase in the text phrases. The method also includes converting the selected text phrase to a selected phrase vector. The method also includes generating a subsequent text phrase, after the selected text phrase, from the ongoing stream of voice data, and adding the subsequent text phrase to the text phrases. The method also includes converting the subsequent text phrase to a subsequent phrase vector. The method also includes generating a similarity confidence score from the selected phrase vector and the subsequent phrase vector, using a machine learning model. The method also includes highlighting, responsive to the similarity confidence score exceeding a threshold value, the subsequent text phrase in the text phrases.

Abstract: The example embodiments are directed toward improvements in document classification. In an embodiment, a method is disclosed comprising generating a set of sentences based on a document; predicting a set of labels for each sentence using a multi-label classifier, the multi-label classifier including a self-attended contextual word embedding backbone layer, a bank of trainable unigram convolutions, a bank of trainable bigram convolutions, and a fully connected layer the multi-label classifier trained using a weakly labeled data set; and labeling the document based on the set of labels. The various embodiments can target multiple use cases such as identifying related entities, trending related entities, creating ephemeral timeline of entities, and others using a single solution. Further, the various embodiments provide a weakly supervised framework to train a model when a labeled golden set does not contain a sufficient number of examples.

Abstract: Implementations relate to proactively aggregating client device data to append to client assistant data that is communicated to a server device in response to a user request to a client automated assistant. When a user request that is associated with, for example, a peripheral client device, is received at a client device, the client device can communicate, to a server device, data that embodies the user request (e.g., audio data and/or local speech recognition data), along with peripheral device data that was received before the client device received the user request. In this way, the client automated assistant can bypass expressly soliciting peripheral device data each time a user request is received at another client device. Instead, a peripheral device can proactively communicate device data to a client device so that the device data can be appended to request data communicated to the server device from a particular client device.

Abstract: A system for receiving a corpus of sign language data in which a plurality of known signs each correspond to known meanings, generate a model for identifying new sign language signs using the corpus, and identifying, using the model, a new sign language sign that does not match any of the plurality of known signs.

Abstract: The present invention relates to a method for voice prompting, a mobile control terminal and a system for voice prompting. The method includes: receiving, by the mobile control terminal, a warning instruction transmitted by an unmanned aerial vehicle (UAV); generating corresponding text information according to the warning instruction; converting the text information to an audio file; and playing the audio file. The mobile control terminal of the present invention provides various warning information to a user by playing an audio file, which is not a preset audio file directly stored in the mobile control terminal, but an audio file converted from text information generated by the mobile control terminal according to a warning instruction transmitted by the UAV. Therefore, there is no need to store a preset audio file in the mobile control terminal, which reduces a memory space occupied in the mobile control terminal and improves performance of the mobile control terminal.

Abstract: A method for computer control of online social interactions based on conversation processing. A portion of a conversation with a given user is recorded, stored in memory, and processed so as to influence subsequent interactions. These may include audio or other contextualized annunciations of subsequent telephone calls. Other modes addressing conversational processing for social purposes can result in interwoven conversation guided among threads of interest, advertising and incented participation of conversational content and placement. The invention is capable of implementation in telecommunications systems such as cellular, local exchange, and VOIP, and in combination with other forms of internet-based telecommunication, including smart phones and adaptive forums chat rooms.

Abstract: A data labeling method and device and a computer-readable storage medium. The method includes: based on a knowledge graph, remotely acquiring data to be labeled; performing data cleaning and pre-labeling to the data to be labeled, to obtain pre-labeled data; and performing labeling correction to the pre-labeled data.

Abstract: A voice interaction method, where a service type set on which a user has a voice interaction intention is predicted based on a target event that can trigger voice interaction, and when a service type of a first service expressed by a voice instruction is a target service type in the service type set, the first service is executed.

Abstract: A method according to one embodiment includes receiving audio input by a microphone of an access control device that controls access through a passageway, processing an audio signal associated with the audio input to identify and authenticate a user, determining a command corresponding with the audio signal in response to identification and authentication of the user, and performing at least one action that corresponds with the command.

Abstract: A system and method for contextualized communication events and notifications for project management based on conversation processing. A portion of a conversation with a given user is recorded, stored in memory, and processed so as to influence subsequent interactions. These may include audio or other contextualized annunciations of related information and expectations concerning project responsibilities, schedules, and dependencies. Subsequent meetings and telephone calls may chivvy the project along through reminders of potential compliance or noncompliance with stated and accepted assignments. Other modes addressing conversational processing for social purposes can result in interwoven conversation guided among threads of interest, advertising and incented participation of conversational content and placement.

Abstract: A Unified Speech and Audio Codec (USAC) that may process a window sequence based on mode switching is provided. The USAC may perform encoding or decoding by overlapping between frames based on a folding point when mode switching occurs. The USAC may process different window sequences for each situation to perform encoding or decoding, and thereby may improve a coding efficiency.

Abstract: Disclosed is a method for a social interaction by a robot device. The method includes receiving an input from a user, determining an emotional state of the user by mapping the received input with a set of emotions and dynamically interacting with the user based on the determined emotional state in response to the input. Dynamically interacting with the user includes generating contextual parameters based on the determined emotional state. The method includes determining an action in response to the received input based on the generated contextual parameters and performing the determined action. The method further includes receiving another input from the user in response to the performed action and dynamically updating the mapping of the received input with the set of emotions based on the other input for interacting with the user.

Abstract: An apparatus for selecting one of a first encoding algorithm having a first characteristic and a second encoding algorithm having a second characteristic for encoding a portion of an audio signal to obtain an encoded version of the portion of the audio signal has a first estimator for estimating a first quality measure for the portion of the audio signal, which is associated with the first encoding algorithm, without actually encoding and decoding the portion of the audio signal using the first encoding algorithm. A second estimator is provided for estimating a second quality measure for the portion of the audio signal, which is associated with the second encoding algorithm, without actually encoding and decoding the portion of the audio signal using the second encoding algorithm. The apparatus has a controller for selecting the first or second encoding algorithms based on a comparison between the first and second quality measures.