Abstract: A voice wake-up method is provided. The method includes: collecting a first voice signal in an environment in which the first electronic device is located. If audio is being played in the environment when the first voice signal is collected, obtaining, in a wired or wireless communication manner, an audio signal corresponding to the audio, determining a first false wake-up result based on the first voice signal and the audio signal; receiving a second false wake-up result sent by the second electronic device; determining a third false wake-up result based on the first false wake-up result and the second false wake-up result; wherein the third false wake-up result is used to indicate whether a wake-up operation needs to be performed on a to-be-woken-up device in a local area network; sending the third false wake-up result to another electronic device other than the first electronic device in the local area network.

Abstract: Disclosed is a method for identifying a word corresponding to a target word in text information, which is performed by one or more processors of a computing device. The method may include: determining a target word; determining a threshold for an edit distance associated with the target word; determining a word of which the edit distance from the target word among words included in text information is equal to or less than the threshold; and identifying the word corresponding to the target word based on the determined word.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: An encoder for encoding an audio signal is configured to encode the audio signal in a transform domain or filter-bank domain, is configured to determine spectral coefficients of the audio signal for a current frame and at least one previous frame, and is configured to selectively apply predictive encoding to a plurality of individual spectral coefficients or groups of spectral coefficients which are separated by at least one spectral coefficient.

Abstract: The present invention provides a system (100) for extending voice commands and determining a user's location. The system (100) includes a plurality of interconnected user devices (10a, 10b, 10c, 10d, 10e, 10f, 10g, 10h, 10i, 10j, . . . ) forming a meshed network (1000), to receive a voice command from a user (1) for extending the range of voice command detection. Further, the system (100) includes a base unit (20) connected to one of the user devices, which receives the voice command from the user device (10) closest to the base unit (20) and calculates ranging data. A master controller (30), connected to multiple base units (20a, 20b, 20c, 20d, . . . ), calculates the user's location based on ranging data, processes the voice command to determine the user's intent, and performs an action based on the determined intent and sends the user's location to a central server (40), to visualize the user's location on map.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: Systems, devices, and methods discussed herein provide improved autonomous agent applications that are configured to generate a diagnosis for input symptoms from labels (e.g., diseases, illnesses, and/or conditions) associated with previously-known cases. Extended discourse trees may be generated that identify multiple discourse trees corresponding to various fragment granularities (e.g., document, paragraph, sentence, phrase, word, etc.) of previously-known cases as well as rhetorical relations between those discourse trees. New symptoms can be provided (e.g., via the autonomous agent) as input. The input can be parsed to identify various fragments of the input and rhetorical relations between the fragments. These fragments can be matched to fragments of previously-known cases by matching nodes of the extended discourse tree.

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: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a dialog system using user feedback. According to one implementation, a method includes receiving, by a dialog engine and from a computing device, a voice input, determining, by the dialog engine, that the received voice input is classified as feedback to an answer of a question; identifying, by the dialog engine, a predetermined feedback scare associated with the feedback; and adjusting a confidence score associated with the question and the answer based on the predetermined feedback score.

Abstract: An audio decoder for providing a decoded audio information includes a arithmetic decoder for providing a plurality of decoded spectral values on the basis of an arithmetically-encoded representation of the spectral values and a frequency-domain-to-time-domain converter for providing a time-domain audio representation using the decoded spectral values. The arithmetic decoder is configured to select a mapping rule describing a mapping of a code value onto a symbol code in dependence on a context state. The arithmetic decoder is configured to determine or modify the current context state in dependence on a plurality of previously-decoded spectral values. The arithmetic decoder is configured to detect a group of a plurality of previously-decoded spectral values, which fulfill, individually or taken together, a predetermined condition regarding their magnitudes, and to determine the current context state in dependence on a result of the detection. An audio encoder uses similar principles.

Abstract: Systems, devices, and methods transcribe words recorded in audio data. A computer-generated transcript is provided. The transcript comprises records for each word in the computer-generated transcript. At least one confirmation input is received for each record. The at least one confirmation input modifies a selected record and automatically identifies a next record for receiving a next confirmation input. A sequence of confirmation inputs may rapidly modify and validate each record in a sequence of records in the computer-generated transcript. A validated transcript is generated from the modified records and is provided from an evidence management system.

Abstract: Aspects of the subject disclosure may include, for example, a method in which a processing system generates a list of communications desired by a user, and determines an attentiveness level of the user with respect to equipment of the user currently in use, to determine whether the user is available to participate in a desired communication. The method also includes accessing equipment of a target party of the communication to determine an attentiveness level of the target party with respect to equipment of the target party, to determine whether the target party is available to participate in the communication. The method also includes generating a notice at the equipment of the user that the target party is available; and initiating the communication between the equipment of the user and the equipment of the target party. Other embodiments are disclosed.

Abstract: Disclosed is a pronunciation transcription method performed by a computing device. The method may include: acquiring a partial audio signal of a first sound unit generated by splitting an audio signal; inputting the partial audio signal of the first sound unit into a pronunciation transcription model; and outputting a pronunciation transcription result for the partial audio signal of the first sound unit by using the pronunciation transcription model.

Abstract: A control device includes at least one memory, and at least one processor configured to detect a voice segment from sound data, the sound data being detected while a controlled object operates, and stop the controlled object based on following conditions: a speaking speed is a predetermined speed threshold or greater, the speaking speed being calculated based on a portion of the sound data in the voice segment; and a length of the voice segment is a predetermined length threshold or less.

Abstract: A method for automatic transmission of status information from a first communications terminal set up for speech communication to a second communications terminal set up for text communication is provided. The speech communication between communications terminals is processed over a speech communications server and the text communication between communications terminals over a text communications server. The speech communications server and the text communications server exchange messages over at least one converter device. The status information will be transmitted from the first communications terminal over the speech communications server, the converter device, and the text communications server to the second communications terminal.

Abstract: The present disclosure provides a method for processing sentence, including: determining a vector representation of a sentence to be processed; inputting the vector representation of the sentence to be processed to a first recurrent neural network, and updating a state of a hidden layer of the first recurrent neural network as a vector representation of a historical dialogue, wherein, if a processed sentence exists, the state of the hidden layer of the first recurrent neural network is related to the processed sentence; determining a knowledge vector related to the sentence to be processed; and generating a reply sentence based on the vector representation of the historical dialogue and the knowledge vector. The present disclosure further provides a system for processing sentence, an electronic device, and a computer-readable medium.

Abstract: Methods, systems, and media for voice-based call operations are provided. In some embodiments, a method comprises: receiving, at a first user device, a communication; detecting a voice command, using the first user device, that includes a keyword; and in response to detecting the voice command, causing the communication to be transferred to a second user device that is associated with the keyword.

Abstract: An electronic apparatus is disclosed. The electronic apparatus includes: a memory storing task information and keyword information regarding a voice recognition agent function related to execution of an application, and a processor connected with the memory and configured to control the electronic apparatus. The processor may be configured to: apply a first weight to first step information including the keyword information among a plurality of step information included in the task information, apply a second weight different from the first weight to second step information including a motion among the plurality of step information, update the task information based on the first step information to which the first weight was applied and the second step information to which the second weight was applied, and based on a voice command for the application being input, execute the voice recognition agent function based on the updated task information.

Abstract: In one embodiment, a method includes maintaining contextual information from a first user request associated with a first task by a context engine, wherein the first task is associated with a first agent, receiving a second user request associated with a second task from a client system, wherein the second user request comprises an ambiguous mention and the second task is associated with a second agent, determining a context carryover is required for the second agent to execute the second task, determining the ambiguous mention corresponds to one or more data items associated with the contextual information from the first user request, and providing the one or more data items to the second agent for execution of the second task.