Abstract: In some implementations, a method includes displaying, on a display, an environment that includes a representation of a virtual agent that is associated with a sensory characteristic. In some implementations, the method includes selecting, based on the sensory characteristic associated with the virtual agent, a subset of a plurality of sensors to provide sensor data for the virtual agent. In some implementations, the method includes providing the sensor data captured by the subset of the plurality of sensors to the virtual agent in order to reduce power consumption of the device. In some implementations, the method includes displaying a manipulation of the representation of the virtual agent based on an interpretation of the sensor data by the virtual agent.

Abstract: A system for an automated real-time transcription and editing of audio data using interim text, including a processor of an audio transcription server (ATS) node configured to host a machine learning (ML) module coupled to at least one audio source entity and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire audio data from the at least one audio source entity; parse out the audio data to derive features for beam forming and features for speaker diarization; generate a set of classifiers based on the features for beam forming and the features for speaker diarization; provide the set of classifiers to the ML module configured to generate a predictive model for producing at least one speaker identification parameter; identify the speaker based on the at least one speaker identification parameter; continuously transcribe the audio data to generate an interim text associated

Abstract: Systems and processes for a multi-modal digital assistant are provided.

Abstract: A method for identifying and executing a voice command in a continuous listening Internet of Things (IoT) environment, may include: receiving, by at least one IoT device, a voice input in the continuous listening IoT environment; detecting, by the at least one IoT device, an occurrence of at least one non-speech event in a vicinity of at least one other IoT device in the continuous listening IoT environment; determining, by the at least one IoT device, an ambient context associated with the at least one non-speech event; determining, by the at least one IoT device, a correlation between the ambient context and the at least one other IoT device based on an event location of the occurrence of the at least one non-speech event; and determining, by the at least one IoT device, presence of at least one voice command within the voice input based on the correlation.

Abstract: A speech synthesis system is described and may include at least one microphone; a speaker; a sensing system, and memory storing processor-executable instructions, which when executed by the processor, cause the processor to: detect speech-related signals emanating from the subject; generate a variable excitation signal; shape the generated variable excitation signal according to previously stored speech recordings; and cause, from the speaker and based on the shaped variable excitation signal, produced speech content that approximates the matched one or more voice characteristics in the previously stored speech recordings.

Abstract: The present disclosure relates to scalable systems and methods for detecting, labeling, and protecting sensitive data in natural language processing (NLP) environments. This includes NLP applications in artificial intelligence (AI) systems, such as language models (LMs) and generative AI (GenAI). More particularly, the present disclosure introduces a hierarchical, context-aware labeling mechanism that is optimized using an LM in conjunction with machine learning (ML) techniques to ensure the utility-preserving effective protection of sensitive data with, for example, minimal false positives and false negatives and/or optimal precision and recall (e.g., in terms of an F1 Score).

Abstract: In aspects of presenting relevant audio data, a mobile device implements an audio playback manager that monitors audio in an environment for a trigger word. The audio playback manager detects the trigger word via a microphone associated with a headset in communication with a mobile device. The audio playback manager determines whether a portion of the audio preceding the trigger word is relevant to a user of the mobile device and presents the portion of the audio that is relevant to the user.

Abstract: An embodiment computer-implemented method for predicting an intention of a user includes receiving from a vehicle first utterance data obtained by converting a voice command of the user into text, performing natural language understanding to attempt to decide the intention of the user from the first utterance data, predicting the intention of the user using stored pattern data in response to failing to decide the intention of the user, wherein the stored pattern data includes a plurality of patterns and confidence generated based on second utterance data received from each of a plurality of vehicles that are unable to decide the intention of the user and subsequent action data, generating a prompt suggesting an operation based on the predicted intention, and transmitting the prompt to the vehicle.

Abstract: In an embodiment, the disclosure relates to a device for assisting a respondent in a conversation. The device includes a microphone configured to detect a voice input, and a transmitter communicatively coupled to a server and configured to transmit the voice input to the server. The server is to generate vectors associated with the voice input, feed the vectors associated with the voice input to an Artificial Intelligence utilizing a trained Machine Learning (ML) model, and obtain, from the trained ML model, an output corresponding to the vectors. The device further includes a receiver communicatively coupled to the server, and configured to receive from the server, the output generated by the ML model. A speaker is communicatively coupled with the receiver and is configured to generate a voice-based response based on the output, for assisting the respondent in responding to the conversation.

Abstract: An approach is disclosed for enabling contextually relevant conversational interaction. Environment data is received by an AI System which detects a plurality of physical objects in a physical environment and forms a contextual understanding of the plurality of physical objects and the physical environment and identifies a user relevant to the contextual understanding. A most relevant contextual information to the user is predicted by the AI system and transformed into a textual form. A set of intents and objectives is predicted by the AI system for user-centered interaction. The AI system and the user interact iteratively through the user-centered interaction to determine an understanding of a most relevant intent and a most relevant objective which is validated by the AI system with the user until the user agrees. The validated most relevant intent and the most relevant objective is utilized to facilitate the user-centered and contextually relevant conversational interaction.

Abstract: A computer-executable Natural Language Understanding configuration generator is configured to generate a Natural Language Understanding configuration. The Natural Language Understanding configuration for a Natural Language Understanding component is based on engineering data related to an asset of an automation system.

Abstract: Techniques are described with respect to a system, method, and computer program product for predicting conversation for multi-party discussions. An associated method includes analyzing a plurality of linguistic inputs; generating an interest graph based on the analysis; extracting a plurality of temporal data from the interest graph; and generating a Post-Salutations Alignment Model (PSAM) based on clustering of the plurality of temporal data.

Abstract: This disclosure describes, in part, context-driven device arbitration techniques to select a speech interface device from multiple speech interface devices to provide a response to a command included in a speech utterance of a user. In some examples, the context-driven arbitration techniques may include executing multiple pipeline instances to analyze audio signals and device metadata received from each of the multiple speech interface devices which detected the speech utterance. A remote speech processing service may execute the multiple pipeline instances and analyze the audio signals and/or metadata, at various stages of the pipeline instances, to determine which speech interface device is to respond to the speech utterance.

Abstract: This application provides a voice interaction method. For a plurality of electronic devices equipped with a same voice assistant, a voice assistant of only one electronic device is in a working mode, and voice assistants of other electronic devices are all in a silent mode. When a user gives a voice for the voice assistant, the other electronic devices whose voice assistants are in the silent mode do not respond to a first instruction triggered by the voice of the user, only the electronic device whose voice assistant is in the working mode sends, according to the first instruction triggered by the voice of the user, a second instruction to an electronic device that is in the plurality of electronic devices and that has a capability of executing a task requested by the user, and the electronic device that receives the second instruction executes the task requested by the user.

Abstract: An apparatus can include an optical device comprising an internal display, a communications system coupled to the optical device, the communications system to receive a wireless signal comprising data representing a textual signal, and a hardware processor to convert the data representing the textual signal into a format for displaying on the digital display. The apparatus can include a text and speech processor for converting signals representing audible speech to visual text and primitive graphics for display on the internal display. The text and speech processor can also convert text to speech for audio output.

Abstract: Generating a semantic synopsis is provided. A dataset comprising structured data is received and semantic analysis of the dataset is performed to determine categories of information included in the datasets. A spatio-temporal analysis of the dataset is also performed to determine time periods and locations to which the dataset applies. A static synopsis of the dataset is then generated in natural language sentences that describes semantic information in the dataset and time periods and locations covered by the dataset. The synopsis can be customized according to a user role and search query data.

Abstract: An information processing device includes a microphone that acquires a speech signal, and at least one processor. In a case in which the processor determines a wake word is included in first recognition data derived from the speech signal, when the processor determines that control information, that is information related to a control processing, is included in second recognition data derived from the speech signal after the wake word, the processor executes a first control processing corresponding to the control information. When the processor determines that the control information is included in third recognition data derived from the speech signal acquired by the microphone during a first period after a predetermined condition related to an event that occurs after the wake word is satisfied or during execution of the first control processing, the processor executes a second control processing corresponding to the control information included in the third recognition data.

Abstract: An electronic device includes a microphone, a memory, and a processor configured to obtain a first natural language understanding result for a first user voice obtained through the microphone based on a first text corresponding to the first user voice, provide a first response corresponding to the first user voice based on the first natural language understanding result, identify whether the first user voice includes a tracking element based on the first natural language understanding result and a second text corresponding to the first response, based on identifying that the first user voice includes the tracking element, store the first text, the first natural language understanding result and the second text in the memory, and obtain a third text corresponding to the first response based on the first natural language understanding result.

Abstract: Audio data, corresponding to an utterance spoken by a person within a detection range of a voice communications device, can include an audio message portion. The audio data can be captured and analyzed to determine the intent to send a message. Based at least in part upon that intent, a remaining portion of the audio data can be analyzed to determine the intended message target or recipient, as well as the portion corresponding to the actual message payload. Once determined, the audio file can be trimmed to the message payload, and the message payload of the audio data can be delivered as an audio message to the target recipient.

Abstract: A method includes receiving text corresponding to a conversation between two or more people, receiving information for use in customizing a summary of the conversation that is to be generated, and generating the summary of the conversation that is customized according to the received information. The summary of the conversation is generated by feeding the received text and the received information into an artificial intelligence model that uses the received text and the received information as inputs. A system includes a network interface and a processor-based system configured to receive text corresponding to a conversation between two or more people, receive information for use in customizing a summary of the conversation that is to be generated, and generate the summary of the conversation that is customized according to the received information.