Abstract: A system and method for temporarily disabling keyword detection to avoid detection of machine-generated keywords. A local device may operate two keyword detectors. The first keyword detector operates on input audio data received by a microphone to capture keywords uttered by a user. In these instances, the keyword may be detected by the first detector and the audio data may be indicated for speech processing. The system may determine output audio data responsive to the input audio data. The local device may process the output audio data to determine that it also includes the keyword. The device may then disable the first keyword detector while the output audio data is played back by an audio speaker of the local device. Thus the local device may avoid detection of a keyword originating from the output audio. The first keyword detector may be reactivated after a time interval during which the keyword might be detectable in the output audio.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using neural networks. A feature vector that models audio characteristics of a portion of an utterance is received. Data indicative of latent variables of multivariate factor analysis is received. The feature vector and the data indicative of the latent variables is provided as input to a neural network. A candidate transcription for the utterance is determined based on at least an output of the neural network.

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: One embodiment provides a method, including: receiving, at an information handling device, text associated with a user command; storing, in a data store, an encrypted form of the text associated with the user command; determining, using a processor, whether the encrypted form of the text has been detected in other user commands in exceedance of a predetermined threshold; and storing, responsive to determining that the encrypted form of the text has been detected in the other user commands in exceedance of the predetermined threshold, an unencrypted transcript of the text in a data table. Other aspects are described and claimed.

Abstract: An incident report management system is configured to receive and analyze incident reports relating to workplace accidents and injuries. A natural language processing function utilizes word dictionaries of varying type and scope to reduce an incident description to a set of core components that are far smaller than the input text while also preserving important aspects of the input text. The reduced core component set may be analyzed for meaning, compared to large volumes of historic incident reports, and otherwise processed more quickly and more efficiently whether by an expert function or AI function. In this manner, the system is able to provide real-time feedback during submission of incidents to improve quality and completeness, and after submission of incidents to notify users of serious incidents, provide dashboard analytics, and identify underlying and undiscovered risks in the workplace.

Abstract: Systems and methods are provided for responding to a natural language query, e.g., a first natural language query. A first natural language understanding model is used to process the natural language query. A confidence level, e.g., a first confidence level, of the understanding of the natural language query is determined. In response to the confidence level being below a confidence level threshold, the natural language query is reprocessed using a reprocessing module. A response to the first natural language query is generated based on the processing of the natural language query by the first natural language understanding model and the reprocessing of the natural language query.

Abstract: A method for constructing a decoding network, a speech recognition method, a device, an apparatus, and a storage medium are provided. The method for constructing a decoding network includes: acquiring a general language model, a domain language model, and a general decoding network generated based on the general language model; generating a domain decoding network based on the domain language model and the general language model; and integrating the domain decoding network with the general decoding network to obtain a target decoding network. The speech recognition method includes: decoding to-be-recognized speech data by using a target decoding network to obtain a decoding path for the to-be-recognized speech data; and determining a speech recognition result for the to-be-recognized speech data based on the decoding path for the to-be-recognized speech data.

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 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: 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: 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: 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: 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 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: 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.