Abstract: A system efficiently selects at least one device from multiple devices based on received audio signals. In some instances, the system receives audio signals from devices that each comprise at least one microphone. A respective audio signal of the audio signals includes a representation of a sound originating from a location. The system then determines a device to be used to respond to the sound. In some instances, the system analyzes times in which the received audio signals that represent the sound are generated and/or volumes of the sound as represented by the received audio signals. The system can then select the device based on the analysis.

Abstract: An embodiment call termination apparatus includes a call termination word existence determination device configured to determine whether a call termination word exists in a call voice of a user, and a controller configured to compare the call termination word of the user with a call termination example word previously registered in a vehicle to control whether to terminate a call.

Abstract: Disclosed is a method for determining important parts among a speech-to-text (STT) result and reference data, which is performed by a computing device. The method may include acquiring STT data generated by performing STT with respect to a speech signal; acquiring reference data; determining first important information in one data among the STT data and the reference data; and determining second important information linked with the first important information in other data different from data in which the first important information is determined among the STT data and the reference data.

Abstract: A computer-implemented technique uses a machine-trained model to convert input information provided by a user into a natural language expression and a semantic vector. The semantic vector provides a distributed representation of the natural language expression. The technique then uses the generated semantic vector to find a target data record having a target semantic vector that is within a prescribed distance to the generated semantic vector. The technique then outputs at least a target natural language expression that is associated with the target data record. The target data record is produced in an offline process that converts one or more attribute values in an existing structured data record into the target semantic vector and the target natural language expression. Also described herein is a technique for training the machine-trained model and for updating existing collections of data records using the machine-trained model.

Abstract: Disclosed are methods and systems for voice phishing monitoring. For instance, a method includes receiving voice data of an incoming call to a communication device from an application associated with a user account and executing on the device, identifying an entity and interaction allegedly associated with the incoming call from the voice data, determining first fraud indicator data based on a number of the incoming call and second fraud indicator data based on a correspondence of user account interaction data to the entity and/or interaction, and providing the voice data to a trained machine learning system to receive third fraud indicator data based on content and/or a voice characteristic identified from the voice data. The method may further include determining a status for the incoming call of fraudulent or confirmed based on the first, second, and third fraud indicator data, and generating a notification indicating the status for display.

Abstract: The present document relates to a method and an electronic device for adjusting an output level of a speaker on the basis of a distance from an external electronic device, and the electronic device may comprise: a speaker; a first communication circuit; a second communication circuit; a processor; and a memory electrically connected to the processor, wherein the memory stores instructions that, when executed, cause the processor to: receive a signal, broadcast by an external electronic device, through the first communication circuit; determine a distance from the external electronic device at least on the basis of the received signal; and adjust, at a designated level, an output level of the speaker for outputting an audio signal received from the external electronic device by using the second communication circuit, at least on the basis of the distance determined to belong to a designated range. In addition, various embodiments are possible.

Abstract: Techniques described herein are directed to arbitrating between multiple potentially-responsive, automated-assistant capable electronic devices to determine which should respond to the user's utterance, and/or which should defer to other electronic device(s). In various implementations, a spoken utterance of a user may be detected at a microphone of a first electronic device, a spoken utterance provided by a user. Sound(s) emitted by additional electronic device(s) may also be detected at the microphone. Each of the sound(s) may encode a timestamp corresponding to detection of the spoken utterance at a respective electronic device. Timestamp(s) may be extracted from the sound(s) and compared to a local timestamp corresponding to detection of the spoken utterance at the first electronic device. Based on the comparison, the first electronic device may either invoke an automated assistant locally or defer to one of the additional electronic devices.

Abstract: In some implementations, a system may capture audio from a call between a calling device and a called device. The system may filter the captured audio to generate a background audio layer. The system may generate an audio footprint that is a representation of sound in the background audio layer. The system may determine that the audio footprint includes a triggering sound footprint based on one or more audio characteristics of the audio footprint. The system may detect synthetic sound based on the audio footprint and after determining that the audio footprint includes the triggering sound footprint, wherein the synthetic sound is indicative of a sound recording. The system may transmit a notification to one or more devices associated with the call based on detecting the synthetic sound.

Abstract: An electronic device obtains audio signals collected by different microphones in a microphone array. The device filters the audio signals using a first filter to obtain a first target beam. The first filter is configured to suppress an interference speech in the audio signals and enhance a target speech in the audio signals. The device filters the audio signals using a second filter to obtain a first interference beam. The second filter is configured to suppress the target speech and enhance the interference speech. The device a second interference beam of the first interference beam using a third filter. The device determines a difference between the first target beam and the second interference beam as a first audio processing output. The device adaptively updates at least one of the second filter and the third filter, and updates the first filter according to the updated second filter and/or third filter.

Abstract: A method for programming a stimulation device of a stimulation system using a programming device includes providing a set of programming commands for the programming device that include a first programming command increasing a stimulation amplitude and a second programming command includes decreasing the stimulation amplitude; receiving a verbal communication by a voice command handler of the programming device or in communication with the programming device; determining whether the verbal communication is a trigger word and, when the verbal communication is the trigger word, entering a triggered state, wherein, after entering the triggered state, the programming device remains in the triggered state until a one of at least one stop condition is met; and, when in the triggered state, determining whether the verbal communication is one of the programming commands and, when the verbal communication is one of the programming commands, executing the one of the programming commands.

Abstract: Various embodiments of the present disclosure relate generally to providing services to users via communication channels. More specifically, various embodiments of the present disclosure relate to systems and methods for modifying, updating, and/or changing communication channel interactions based on the tracking or listening for events within other communication channels.

Abstract: A non-linear echo is allowed to be suppressed and voice deterioration is allowed to be suppressed. An echo suppression device for suppressing an echo generated by inputting a voice signal output from a speaker to a microphone includes a compressor on a receiving signal path for transmitting a signal of a receiving signal to the speaker. When a double-talk state has been detected, the compressor performs a compression process on a signal greater than a first threshold among the receiving signals.

Abstract: Methods, systems, and computer-readable media for rapid event voice documentation are provided herein. The rapid event voice documentation system captures verbalized orders and actions and translates that unstructured voice data to structured, usable data for documentation. The voice data captured is tagged with metadata including the name and role of the speaker, a time stamp indicating a time the data was spoken, and a clinical concept identified in the data captured. The system automatically identifies orders (e.g., medications, labs and procedures, etc.), treatments, and assessments/findings that were verbalized during the rapid event to create structured data that is usable by a health information system and ready for documentation directly into an EHR. The system provides all of the captured data including orders, assessment documentation, vital signs and measurements, performed procedures, and treatments, and who performed each, available for viewing and interaction in real time.

Abstract: A system and method for omnichannel text-based routing, transfer, and customer-to-enterprise communications with smart masking of personal information. The system is a cloud-based network containing an interaction control server, a routing engine, an optimization engine, a media translation server, a mobile application, a business entity portal, mobile and compute devices for business enterprise and customer, and an enterprise database. Taken together or in part, said system optimizes customer interactions by delivering context aware text message, chat or email to customers; and when requested by customer, optimally routes with context, based upon a multitude of variables, to best available resource to handle the customer interaction. The system may be accessed through web browsers or purpose-built computer and mobile phone applications.

Abstract: A server is provided. The server includes a communication circuitry, and at least one processor operatively connected with the communication circuitry. The at least one processor may be configured to, in response to traffic of a plurality of speeches to wake up a voice assistant feature, received within a preset period being a preset value or more, generate a plurality of clusters based on similarities between the plurality of speeches, and determine whether to respond to each of speeches included in each of the plurality of clusters based on similarities between the speeches included in each of the plurality of clusters.

Abstract: An example method involves monitoring, by a computing device, a communications feed for an indication of media and detecting, in the communications feed, the indication of the media. The method may further involve identifying at least one media item corresponding to the indication of the media and causing a playback queue of a media playback system to include one or more of the identified at least one media item.

Abstract: A device and method for determining at least one part of a knowledge graph. A body of text is made available; for one sentence from the body of text, a first, second, and third input respectively for a first, second, and third classifier is determined. Each of the first, second, and third inputs includes a numerical representation of at least one part of the sentence. A numerical representation of a first probability is determined by the first classifier as a function of the first input, which indicates whether or not the sentence relates to the knowledge graph. If the numerical representation of the first probability satisfies a first condition, a numerical representation of a second probability is determined by the second classifier as a function of the second input, which defines a first type for the word from the sentence.

Abstract: An example method is described. The method includes receiving, by a computing system, data indicative of a call into an interactive voice response (IVR) system from a user device and determining, by the computing system and based on the data, a set of actions performed by the user device within the IVR system and a corresponding set of results performed by the IVR system during the call. Additionally, the method includes converting, by the computing system, the set of actions and the corresponding set of results into a sequence of code pairs using a dictionary established based on training data, determining, by the computing system, an activity pattern during the call based on the sequence of code pairs; and calculating, by the computing system, a probability that the call is fraudulent based on the activity pattern during the call.

Abstract: A method for enhancing an audio signal in a full duplex communication system includes the steps of: a) receiving a far end audio signal from a communication input; b) forwarding the far end audio signal to an audio speaker; c) receiving a near end audio signal from a microphone; d) performing an echo reduction on the received near end audio signal; e) forwarding the near end audio signal after the echo reduction to a communication output; f) determining whether a residual echo still exists in the near end audio signal after the echo reduction; and g) attenuating the near end audio signal or deactivating its forwarding to the communication output if the existence of residual echo is determined.

Abstract: The occurrence cost of unknown words that are not registered in a morphological analysis dictionary is calculated by applying an occurrence cost regression model, which is a learning model. An information processing device includes a notation feature amount extraction unit that extracts a notation feature amount of a character string, a part-of-speech feature amount extraction unit that extracts a part-of-speech feature amount of the character string, and an occurrence cost estimation unit that receives the notation feature amount and the part-of-speech feature amount and calculates an occurrence cost of the character string by applying an occurrence cost regression model. The occurrence cost regression model is a learning model that estimates the occurrence cost from the notation feature amount and the part-of-speech feature amount of a character string, generated by a learning process using registration data of an existing morphological analysis dictionary as teacher data.