Abstract: Provided are an image display apparatus and a method of controlling the same. The image display apparatus enabling voice recognition includes: a first voice inputter which receives a user-side audio signal; an audio outputter which outputs an audio signal processed by the image display apparatus; a first voice recognizer which recognizes the user-side audio signal received through the first voice inputter; and a controller which decreases a volume of the audio signal output through the audio outputter to a predetermined level if a voice recognition start command is received.

Abstract: A spectrum filler for filling non-coded residual sub-vectors of a transform coded audio signal includes a sub-vector compressor configured to compress actually coded residual sub-vectors. A sub-vector rejecter is configured to reject compressed residual sub-vectors that do not fulfill a predetermined sparseness criterion. A sub-vector collector is configured to concatenate the remaining compressed residual sub-vectors to form a first virtual codebook. A coefficient combiner is configured to combine pairs of coefficients of the first virtual codebook to form a second virtual codebook. A sub-vector filler is configured to fill non-coded residual sub-vectors below a predetermined frequency with coefficients from the first virtual codebook, and to fill non-coded residual sub-vectors above the predetermined frequency with coefficients from the second virtual codebook.

Abstract: A method and apparatus for controlling learning of a model for estimating an intention of an input utterance is disclosed. A method of controlling learning of a model for estimating an intention of an input utterance among a plurality of intentions includes providing a first index corresponding to the number of registered utterances for each intention, providing a second index corresponding to a learning level for each intention, providing a learning target setting interface such that at least one intention that is to be a learning target is selected from among the intentions based on the first index and the second index, and training the model based on the registered utterances for each intention and setting of the learning target for each intention.

Abstract: Systems and techniques that facilitate spurious relationship filtration from external knowledge graphs based on distributional semantics of an input corpus are provided. In one or more embodiments, a context component can generate a context-based word embedding of one or more first terms in a document collection. The embedding can yield vector representations of the one or more first terms. The one or more first terms can correspond to knowledge terms in one or more first nodes of a knowledge graph. In one or more embodiments, a filtering component can filter out a relationship between the one or more first nodes and a second node of the knowledge graph based on a similarity value being less than a threshold. The similarity value can be a function of the vector representations of the one or more first terms. In various embodiments, cosine similarity can be used to compute the similarity value.

Abstract: A text string with a first and a second portion is provided. A domain of the text string is determined by applying a first word-matching process to the first portion of the text string. It is then determined whether the second portion of the text string matches a word of a set of words associated with the domain by applying a second word-matching process to the second portion of the text string. Upon determining that the second portion of the text string matches the word of the set of words, it is determined whether a user intent from the text string based at least in part on the domain and the word of the set of words.

Abstract: Systems and methods disclosed herein include (i) receiving a voice command via at least one microphone of a networked microphone device, wherein the networked microphone device is configured to receive voice commands for a media playback system, and wherein the media playback system comprises the networked microphone device and a first playback device configured to play back content, (ii) determining that the networked microphone device is not configured to play back the content, (iii) in response to determining that the networked microphone is not configured to play back the content, determining that the first playback device is available to play back the content, (iv) causing the first playback device to play back the content, (v) determining that the first playback device is no longer available to play back the content, and (vi) selecting a second playback device to play back the content.

Abstract: Embodiments include methods, devices, systems, and non-transitory process-readable storage media for voice-activated message filtering rule generation. Some embodiments may include receiving a spoken command from a communication device, parsing the spoken command to identify an element of the spoken command, generating a message rule based on the identified element of the spoken command, determining whether the generated message rule has been met, and sending a message to the communication device in response to determining that the message rule has been met.

Abstract: An omni-channel orchestrated conversation system and virtual conversation agent for realtime contextual and orchestrated omni-channel conversation with a human and an omni-channel orchestrated conversation process for conducting realtime contextual and fluid conversation with a human by a virtual conversation agent in relation to a particular domain are disclosed.

Abstract: A processor-implemented method for automatically determining a mismatch between a sentiment and a polarity of a life situation using an Artificial Intelligence (AI) model during a conversation with an AI chatbot is provided. The method includes (i) determining at least one sentiment of the user using a sentiment detecting AI model, (ii) predicting life situation from the conversation between the AI chatbot and the user using an intent recognition AI model, (ii) determining, a gravity and a polarity of the life situation using a life events scale, (iii) comparing the at least one sentiment of the user to the polarity of the life situation when the life situation is determined to the high gravity, and (iv) automatically determining the mismatch between the at least one sentiment of the user and the polarity of the at least one life situation of the high gravity situation.

Abstract: A virtual environment platform may receive, from a user device, a request to access a virtual reality (VR) environment and may verify, based on the request, a user of the user device to allow the user device access to the VR environment. The virtual environment platform may receive, after verifying the user of the user device, user voice input and user handwritten input from the user device. The virtual environment platform may generate processed user speech by processing the user voice input, wherein a characteristic of the processed user speech and a corresponding characteristic of the user voice input are different and may generate formatted user text by processing the user handwritten input, wherein the formatted user text is machine-encoded text. The virtual environment platform may cause the processed user speech to be audibly presented and the formatted user text to be visually presented in the VR environment.

Abstract: Provided is an encoding apparatus for integrally encoding and decoding a speech signal and a audio signal, and may include: an input signal analyzer to analyze a characteristic of an input signal; a stereo encoder to down mix the input signal to a mono signal when the input signal is a stereo signal, and to extract stereo sound image information; a frequency band expander to expand a frequency band of the input signal; a sampling rate converter to convert a sampling rate; a speech signal encoder to encode the input signal using a speech encoding module when the input signal is a speech characteristics signal; a audio signal encoder to encode the input signal using a audio encoding module when the input signal is a audio characteristic signal; and a bitstream generator to generate a bitstream.

Abstract: A guide voice output control system includes a voice output control unit having a function of outputting a guide voice in response to a trigger and a function of executing interaction related processing having a reception stage for receiving voice, a recognition stage for recognizing voice, and an output stage for outputting voice based on a recognition result, in which the voice output control unit controls the output of the guide voice according to the processing stage of the interaction related processing when the trigger is generated during the execution of the processing, and dynamically controls the output of the guide voice according to whether or not the processing stage is a stage that does not affect the accuracy of voice recognition or listening difficulty of a user even if the guide voice is output.

Abstract: Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.

Abstract: In some aspects, a computing device receives a scan of a code displayed on an order post located near a restaurant, determines that the code is associated with the restaurant, and automatically opens a software application and navigates the software application to an ordering page associated with the restaurant. The computing device initiates receiving, via the software application, input associated with an order, sends the input to a machine learning based software agent executing on a server, receives a predicted response to the input, provides the predicted response as audio output and/or displays the predicted response on the touchscreen display device. After the order is complete, the computing device sends order data associated with the order to the restaurant. After receiving an indication from the restaurant that the order is ready, the computing device indicates that the order is ready to be picked up.

Abstract: In order to detect a replay attack in a speaker recognition system, at least one feature is identified in a detected magnetic field. It is then determined whether the at least one identified feature of the detected magnetic field is indicative of playback of speech through a loudspeaker. If so, it is determined that a replay attack may have taken place.

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.

Abstract: An apparatus for generating an enhanced signal from an input signal, wherein the enhanced signal has spectral values for an enhancement spectral region, the spectral values for the enhancement spectral regions not being contained in the input signal, includes a mapper for mapping a source spectral region of the input signal to a target region in the enhancement spectral region, the source spectral region including a noise-filling region; and a noise filler configured for generating first noise values for the noise-filling region in the source spectral region of the input signal and for generating second noise values for a noise region in the target region, wherein the second noise values are decorrelated from the first noise values or for generating second noise values for a noise region in the target region, wherein the second noise values are decorrelated from first noise values in the source region.

Abstract: A method for representing a second presentation of audio channels or objects as a data stream, the method comprising the steps of: (a) providing a set of base signals, the base signals representing a first presentation of the audio channels or objects; (b) providing a set of transformation parameters, the transformation parameters intended to transform the first presentation into the second presentation; the transformation parameters further being specified for at least two frequency bands and including a set of multi-tap convolution matrix parameters for at least one of the frequency bands.

Abstract: An operation method of a dialog agent includes obtaining an utterance history including at least one of an outgoing utterance to be transmitted to request a service or at least one of an incoming utterance to be received to request the service, updating a requirement specification including items requested for the service based on the utterance history, generating utterance information to be used to request the service based on the updated requirement specification, and outputting the generated utterance information.

Abstract: The present disclosure describes techniques for dynamically determining when information is to be output to a user, as well as what information is to be output to a user. A natural language processing system may receive, from a first device, first data representing information to be output at a first point during a skill session. The natural language processing system may also receive, from a second device, second data representing a natural language input. The natural language processing system may determine a skill component is to execute with respect to the natural language input. The natural language processing system may send, to the skill component, second data representing the natural language input. The natural language processing system may receive, from the skill component, an indication that an ongoing first skill session with the second device has reached the first point.