Abstract: Provided is a method of performing hybrid translation using a general-purpose neural network machine translator by a device, including: (a) receiving an original document configured of a first language from a user terminal; (b) determining a field of the original document by comparing the original document with a standard document for each field stored in a database; (c) generating a primary translation configured of a second language for the original document by using the general-purpose neural network machine translator; and (d) converting some expressions included in the primary translation into expressions in another second language with reference to a professional translation dictionary for the determined field to generate a secondary translation, wherein the expression includes any one of a word, a phrase, and a sentence.

Abstract: Disclosed is a display device capable of classifying clickable content in a web application screen, and the display device can perform primary classification on all nodes existing in a document object model (DOM) and perform secondary classification for determining whether a node exists in the screen.

Abstract: An electronic device and method are disclosed. The electronic device includes a microphone, a speaker and a processor. The processor implements the method, including: receiving a first voice input through the microphone, outputting a response related to a function through the speaker, based on the first voice input, when a second voice input is received through the microphone during output of the response, interrupting the output of the response and continue to receive the second voice input, and executing the function based on content of the received second voice input and content of the response output up to one of: a first time point when the second voice input was initially received, and a second time point when the output of the response was interrupted.

Abstract: To improve prediction accuracy of utterance types in a dialog. A learning data generation device (10) according to the present invention comprises: a sort unit (11) configured to perform, based on information appended to utterances in a dialog amongst more than one speaker and that is indicative of a dialogue scene that is a scene in which the utterances in the dialog were made, sorting regarding whether the utterances are to be targets for generation of the learning data, wherein the sorter (11) is configured to exclude utterances of a dialogue scene that includes utterances similar to utterance of the particular type from the targets for generation of learning data.

Abstract: According to various embodiments, an electronic device performing an operation corresponding to an utterance of a user in a multi-device environment includes a communication module, a processor, and a memory. The processor operates a first electronic device for receiving and a second electronic device for executing in association with each other. The memory stores instructions that, when being executed, cause the processor to: determine whether a first utterance received at the first electronic device should be performed at the second electronic device; when the first utterance should be performed at the second electronic device, form a connection binding with the first electronic device and the second electronic device by using one or more channels through the communication module. When an operation corresponding to the first utterance is completed at the second electronic device, the processor terminates the connection binding with the first electronic device and the second electronic device.

Abstract: An In-Car Communication (ICC) system supports the communication paths within a car by receiving the speech signals of a speaking passenger and playing it back for one or more listening passengers. Signal processing tasks are split into a microphone related part and into a loudspeaker related part. A sound processing system suitable for use in a vehicle having multiple acoustic zones includes a plurality of microphone In-Car Communication (Mic-ICC) instances coupled and a plurality of loudspeaker In-Car Communication (Ls-ICC) instances. The system further includes a dynamic audio routing matrix with a controller and coupled to the Mic-ICC instances, a mixer coupled to the plurality of Mic-ICC instances and a distributor coupled to the Ls-ICC instances.

Abstract: An electronic apparatus includes: a memory storing a first threshold value and a second threshold value corresponding to a receiving direction of a wake-up word, a sound receiver comprising sound receiving circuitry, and a processor configured to: identify a receiving direction of the sound based on a sound received through the sound receiver, based on a similarity between sound data obtained in response to the received sound and the wake-up word being greater than or equal to the first threshold value corresponding to the identified receiving direction, perform voice recognition for a subsequent sound received through the sound receiver, and based on the similarity being less than the first threshold value and greater than or equal to the second threshold value, change the first threshold value.

Abstract: A method for combining hotwords in a single utterance receives, at a first assistant-enabled device (AED), audio data corresponding to an utterance directed toward the first AED and a second AED among two or more AEDs where the audio data includes a query specifying an operation to perform. The method also detects, using a hotword detector, a first hotword assigned to the first AED that is different than a second hotword assigned to the second AED In response to detecting the first hotword, the method initiates processing on the audio data to determine that the audio data includes a term preceding the query that at least partially matches the second hotword assigned. Based on the at least partial match, the method executes a collaboration routine to cause the first AED and the second AED to collaborate with one another to fulfill the query.

Abstract: Aspects of the disclosure relate to computing platforms that utilize improved natural language processing techniques for claims processing. A computing platform may receive an audio file and determine one or more utterance segments based on the audio file. The computing platform may generate and send one or more audio chunks based on the one or more utterance segments to an audio transcription system. The computing platform may receive one or more text segments in response and may generate an audio transcription file accordingly. The computing platform may assign a category to each word in the audio transcription file, and may send user interface information to a user device along with one or more commands directing the user device to generate a user interface based on the user interface information, which may cause the user device to display the user interface.

Abstract: Disclosed example people monitoring methods include detecting a first watermark in a first audio signal obtained from an acoustic sensor, the first watermark identifying media presented by a monitored media device, determining whether a second watermark, different from the first watermark, is embedded in the first audio signal obtained from the acoustic sensor, the second watermark identifying at least one of a mobile device or a user of the mobile device, classifying the second watermark as a media watermark or a people monitoring watermark based on a characteristic of the second watermark, and when the second watermark is determined to be embedded in the first audio signal, reporting at least one of the second watermark or information decoded from the second watermark to identify at least one of the mobile device or the user of the mobile device as being exposed to the media presented by the monitored media device.

Abstract: A system and method for voice control of a media playback device is disclosed. The method includes receiving an instruction of a voice command, converting the voice command to text, transmitting the text command to the playback device, and having the playback device execute the command. An instruction may include a command to play a set of audio tracks, and the media playback device plays the set of audio tracks upon receiving the instruction.

Abstract: Offered is a system that presents on a display screen a translation of a sentence together with an untranslated version of the sentence, and that can cause both of the displayed sentences to break apart into component parts in response to a simple user action, e.g., double-tapping on one of them. When the user selects (e.g., taps on) any portion of either version of the sentence, the system can identify a corresponding portion of the other version (in the other language). In some implementations, a user device can include both a microphone and a display screen, and an automatic speech recognition (ASR) engine can be used to transcribe the user's speech in one language (e.g., English) into text. The system can translate the resulting text into another language (e.g., Spanish) and display the translated text on the display screen along with the untranslated text. When a user selects a portion of a sentence, the system can also present information about the selected portion (e.g.

Abstract: In one aspect, a network microphone device includes a plurality of microphones and is configured to capture a voice input via the one or more microphones, detect a wake word in the voice input, transmit data associated with the voice input to one or more remote computing devices associated with a voice assistant service, and receive a response from the one or more remote computing devices, the response comprising a playback command based on the voice input. The network microphone device may be configured to obtain verification information characterizing the voice input and, based on the verification information indicating that the voice input was spoken by an unverified user, functionally disable the NMD from performing the playback command.

Abstract: A system, method, and computer program product for implementing cognitive natural language processing software framework optimization is provided. The method includes receiving instructions associated with an audible user input of a user. An AI input intention of the user is determined and key information is extracted from the audible user input. The key information is inputted into a generated database table and additional key information is retrieved from a dialog table. A supplementary database table comprising the additional key information is generated and the key information is spliced with the additional key information. A resulting spliced data structure is merged into a final database table and natural language is converted into a request code structure within an SQL structure and an interactive AI interface presenting results of the converting is generated. Operational functionality of an AI device is enabled for audibly presenting results of the conversion.

Abstract: A method includes a first assistant-enabled device (AED) receiving an assignment instruction assigning a group hotword to a selected group of AEDs that includes the first AED and one or more other AEDs. Each AED is configured to wake-up from a low-power state when the group hotword is detected in streaming audio by at least one of the AEDs. The method also includes receiving audio data that corresponds to an utterance spoken by the user and includes a query that specifies an operation to perform. In response to detecting the group hotword in the audio data, the method also includes triggering the first AED to wake-up from the low-power state and executing a collaboration routine to cause the first AED and each other AED in the selected group of AEDs to collaborate with one another to fulfill performance of the operation specified by the query.

Abstract: In an embodiment, a method includes receiving a plurality of first images including at least a mouth-related portion of a human uttering a voice, wherein each first image has depth information; obtaining a noisy spectrogram including a first representation of the voice of the human; extracting a plurality of visual features using the first images, wherein one of the visual features is obtained using the depth information of a second image of the first images; extracting a plurality of audio features using the noisy spectrogram; determining a first spectrogram using the visual features and the audio features; subtracting the first spectrogram from the noisy spectrogram, to obtain a purified representation of the voice of the human; and outputting, by an I/O-related outputting device, a response using the purified representation of the voice of the human.

Abstract: A system for user initiated generic conversation with an artificially intelligent machine is provided. The system includes a conversational server (CS) that executes a conversational architecture across multiple devices, a communication network and a remote device. The conversational architecture includes one or more conversational nodes connected by edges which encapsulates flow and logic and transport data between the one or more conversational nodes. The conversational server (CS) receives input, at an input node, from a user through an input modality and performs computation logic that generates output data to pass to an output node.

Abstract: A system and method for the management of multiple digital assistants enabling the collection and analysis of voice commands and the selective instruction of the digital assistant having the most appropriate capability and/or connectivity to respond to the analyzed voice command. In a preferred embodiment the digital assistants are networked via a DAC. The controller serves to as a central node programmed to recognize the voice commands received by networked digital assistants and determine which assistant or assistants are best qualified to respond. This determination is a function of the command type, as well as the location and capabilities of each digital assistant. In addition, the system and method enhance the utility of the digital assistants, enabling a voice command received by a particular digital assistant to be acted upon by one or more digital assistants located outside of the audible range of the spoken voice command.

Abstract: A process of deploying a virtual assistant at an electronic media device to obtain information related to a public-safety incident. An electronic computing device captures a real-time digital audio and/or video stream from a first user under interrogation in relation to the incident and further processes the captured stream to determine that a second user has potential information related to the incident. The electronic computing device then determines that a virtual assistant is not currently deployed at an electronic media device that is identified as positioned at a current location of the second user. When it is determined that the electronic media device is capable of hosting a virtual assistant, the electronic computing device transmits an instruction to deploy a virtual assistant at the electronic media device and further perform an interrogation of the second user to obtain information related to the public-safety incident via the deployed virtual assistant.

Abstract: Coordinating signal processing among computing devices in a voice-driven computing environment is provided. A first and second digital assistant can detect an input audio signal, perform a signal quality check, and provide indications that the first and second digital assistants are operational to process the input audio signal. A system can select the first digital assistant for further processing. The system can receive, from the first digital assistant, data packets including a command. The system can generate, for a network connected device selected from a plurality of network connected devices, an action data structure based on the data packets, and transmit the action data structure to the selected network connected device.