Abstract: A decoding device includes: a separating unit separating first encoded data, a spectrum including a low-band spectrum of audio signals having been encoded, and second encoded data, a high-band spectrum of a higher band having been encoded, based on the first encoded data; a first decoding unit decoding the first encoded data and generating a first decoded spectrum; a first amplitude normalizer dividing amplitude of the first decoded spectrum into sub-bands, normalizing the spectrum of each sub-band by the largest amplitude of the first decoded spectrum within each sub-band, and generating a normalized spectrum; an addition unit adding noise spectrum to the normalized spectrum and generating a noise-added normalized spectrum; a second decoding unit decoding the second encoded data using the noise-added normalized spectrum, and generating a second noise-added spectrum; and a converter performing time-frequency conversion regarding a spectrum coupled based on the first decoded spectrum and second noise-added spe

Abstract: Configurable core domains of a speech processing system are described. A core domain output data format for a given command is originally configured with default content portions. When a user indicates additional content should be output for the command, the speech processing system creates a new output data format for the core domain. The new output data format is user specific and includes both default content portions as well as user preferred content portions.

Abstract: A method, computer program product, and computing system for monitoring a plurality of conversations within a monitored space to generate a conversation data set; processing the conversation data set using machine learning to: define a system-directed command for an ACI system, and associate one or more conversational contexts with the system-directed command; detecting the occurrence of a specific conversational context within the monitored space, wherein the specific conversational context is included in the one or more conversational contexts associated with the system-directed command; and executing, in whole or in part, functionality associated with the system-directed command in response to detecting the occurrence of the specific conversational context without requiring the utterance of the system-directed command and/or a wake-up word/phrase.

Abstract: Disclosed is a method for a social interaction by a robot device. The method includes receiving an input from a user, determining an emotional state of the user by mapping the received input with a set of emotions and dynamically interacting with the user based on the determined emotional state in response to the input. Dynamically interacting with the user includes generating contextual parameters based on the determined emotional state. The method includes determining an action in response to the received input based on the generated contextual parameters and performing the determined action. The method further includes receiving another input from the user in response to the performed action and dynamically updating the mapping of the received input with the set of emotions based on the other input for interacting with the user.

Abstract: An audio signal transmission device for encoding an audio signal includes an audio encoding unit that encodes an audio signal and a side information encoding unit that calculates and encodes side information from a look-ahead signal. An audio signal receiving device for decoding an audio code and outputting an audio signal includes: an audio code buffer that detects packet loss based on a received state of an audio packet, an audio parameter decoding unit that decodes an audio code when an audio packet is correctly received, a side information decoding unit that decodes a side information code when an audio packet is correctly received, a side information accumulation unit that accumulates side information obtained by decoding a side information code, an audio parameter missing processing unit that outputs an audio parameter upon detection of audio packet loss, and an audio synthesis unit that synthesizes decoded audio from the audio parameter.

Abstract: Embodiments generally relate placing a voice response system into a forced sleep state. In some embodiments, a method includes receiving a voice command from a given user to place a voice response system in a woke state. The method further includes obtaining current context data, and analyzing the current context data using a voice response model trained using a voice response corpus that incorporated a history of interactions and context data by one or more users with the voice response system. The method further includes, from the analysis of the current context data using the voice response model, determining whether the voice response system is to be placed in the woke state; and, responsive to determining that the voice response system is not to be placed in the woke state, placing the voice response system in a sleep state contrary to the voice command.

Abstract: Disclosed are a speech recognition verification device and a speech recognition verification method, which verify speech recognition results by executing artificial intelligence (AI) algorithms and/or machine learning algorithms in a 5G environment connected for Internet-of-Things. According to an embodiment, the speech recognition verification method includes converting a verification target text item to a verification target spoken utterance by applying a preset utterance condition, analyzing the verification target spoken utterance and outputting a recognition result text item corresponding to an analysis result, and verifying speech recognition performance through comparison between the verification target text item and the recognition result text item. According to the present disclosure, the speech recognition result may be verified objectively by using a spoken utterance generated with random text and various utterance conditions as input of speech recognition.

Abstract: The present teaching relates to method, system, medium, and implementations for detecting a source of speech sound in a dialogue. A visual signal acquired from a dialogue scene is first received, where the visual signal captures a person present in the dialogue scene. A human lip associated with the person is detected from the visual signal and tracked to detect whether lip movement is observed. If lip movement is detected, a first candidate source of sound is generated corresponding to an area in the dialogue scene where the lip movement occurred.

Abstract: A method comprising recognizing a user utterance including an ambiguity. The method further comprises using a previously-trained code-generation machine to produce, from the user utterance, a data-flow program including a search-history function. The search-history function is configured to select a highest-confidence disambiguating concept from one or more candidate concepts stored in a context-specific dialogue history.

Abstract: A computer parses the document to identify a citation, where the citation serves as a pointer to a source reference. The computer determines a location in the document of a textual assertion associated with the citation. The computer calculates relevancy scores between the textual assertion and a corresponding source reference and between the textual assertion and at least one alternate source reference, where the relevancy scores are determined based at least in part on a machine learning algorithm trained with a plurality of training samples. The computer generates a suggested list of at least one of the source references or at least one alternate source reference based on the relevancy scores calculated by the machine learning algorithm and adds a training sample to the plurality of training samples of the machine learning algorithm in response to an action by a user responsive to the suggested list.

Abstract: An audio signal transmission device for encoding an audio signal includes an audio encoding unit that encodes an audio signal and a side information encoding unit that calculates and encodes side information from a look-ahead signal. An audio signal receiving device for decoding an audio code and outputting an audio signal includes: an audio code buffer that detects packet loss based on a received state of an audio packet, an audio parameter decoding unit that decodes an audio code when an audio packet is correctly received, a side information decoding unit that decodes a side information code when an audio packet is correctly received, a side information accumulation unit that accumulates side information obtained by decoding a side information code, an audio parameter missing processing unit that outputs an audio parameter upon detection of audio packet loss, and an audio synthesis unit that synthesizes decoded audio from the audio parameter.

Abstract: An audio signal transmission device for encoding an audio signal includes an audio encoding unit that encodes an audio signal and a side information encoding unit that calculates and encodes side information from a look-ahead signal. An audio signal receiving device for decoding an audio code and outputting an audio signal includes: an audio code buffer that detects packet loss based on a received state of an audio packet, an audio parameter decoding unit that decodes an audio code when an audio packet is correctly received, a side information decoding unit that decodes a side information code when an audio packet is correctly received, a side information accumulation unit that accumulates side information obtained by decoding a side information code, an audio parameter missing processing unit that outputs an audio parameter upon detection of audio packet loss, and an audio synthesis unit that synthesizes decoded audio from the audio parameter.

Abstract: Various implementations relate to techniques, for controlling smart devices, that are low latency and/or that provide computational efficiencies (client and/or server) and/or network efficiencies. Those implementations relate to generating and/or utilizing cache entries, of a cache that is stored locally at an assistant client device, in control of various smart devices (e.g., smart lights, smart thermostats, smart plugs, smart appliances, smart routers, etc.). Each of the cache entries includes a mapping of text to one or more corresponding semantic representations.

Abstract: Embodiments provide a voice interaction method, a device, and a storage medium. The method includes: transmitting obtained audio data of a user to a server for semantic understanding, to obtain structured data; receiving the structured data returned by the server; and controlling, according to a running game and the structured data, the game to perform a corresponding operation. In the embodiments, voice recognition and semantic understanding technologies are used to enable a user to complete an operation of a game under a dialogue interaction through a communication between a terminal device and a server, thus enhancing game experience of the user and improving entertainment and convenience.

Abstract: Network source identification via audio signals is provided. A system receives data packets with an input audio signal from a client device. The system identifies a request. The system selects a digital component provided by a digital component provider device. The system identifies audio chimes stored in memory of the client device. The system matches, based on a policy, an identifier of the digital component provider device to a first audio chime stored in the memory of the client device. The system determines, based on a characteristic of the first audio chime, a configuration to combine the digital component with the first audio chime. The system generates an action data structure with the digital component, an indication of the first audio chime, and the configuration. The system transmits the action data structure to the client device to cause the client device to generate an output audio signal.

Abstract: An audio signal transmission device for encoding an audio signal includes an audio encoding unit that encodes an audio signal and a side information encoding unit that calculates and encodes side information from a look-ahead signal. An audio signal receiving device for decoding an audio code and outputting an audio signal includes: an audio code buffer that detects packet loss based on a received state of an audio packet, an audio parameter decoding unit that decodes an audio code when an audio packet is correctly received, a side information decoding unit that decodes a side information code when an audio packet is correctly received, a side information accumulation unit that accumulates side information obtained by decoding a side information code, an audio parameter missing processing unit that outputs an audio parameter upon detection of audio packet loss, and an audio synthesis unit that synthesizes decoded audio from the audio parameter.

Abstract: A voice-controlled device includes a beamformer for determining audio data corresponding to one or more directions and a beam selector for selecting in which direction a source of target audio lies. The device determines magnitude spectrums for each beam and for each frequency bin in each beam for each frame of audio data. The device determines frame-by-frame changes in the magnitude and filters the changes to smooth them. The device selects the beam having the greatest smoothed change in magnitude as corresponding to speech.

Abstract: Techniques for improving routing of natural language inputs, of a natural language processing (NLP) system, are described. A natural language input may be routed based on the device that captured the natural language input. A device manufacturer, hospitality provider, business, etc. may cause the NLP system to generate a skill component specific to the device manufacturer, hospitality provider, business, etc. Thereafter, when a natural language input is received from the device, the NLP system may route the natural language input to the device manufacturer-, hospitality provider-, business-, etc.-specific skill component for processing.

Abstract: Disclosed herein is an NLP system that is able to extract meaning from a natural language message using improved parsing techniques. Such an NLP system can be used in concert with an NLG system to interactively interpret messages and generate response messages in an interactive conversational stream. The parsing can include (1) named entity recognition that contextualizes the meanings of words in a message with reference to a knowledge base of named entities understood by the NLP and NLG systems, (2) syntactically parsing the message to determine a grammatical hierarchy for the named entities within the message, (3) reduction of recognized named entities into aggregations of named entities using the determined grammatical hierarchy and reduction rules to further clarify the message's meaning, and (4) mapping the reduced aggregation of named entities to an intent or meaning, wherein this intent/meaning can be used as control instructions for an NLG process.

Abstract: A method, system, and computer program product for applying a profile to an assistive device based on a multitude of cues includes: gathering audio inputs surrounding an assistive device; analyzing, by the assistive device, the audio inputs; determining, based on the analyzing, scenario cues; classifying a current environment surrounding the assistive device from the scenario cues; comparing the current environment to device profiles of the assistive device; determining, based on the comparing, a matching profile; and, in response to determining the matching profile, executing the matching profile on the assistive device.