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: A computer implemented method for analyzing media content includes a step of providing a plurality of narrative files formatted in human readable format. Each narrative file includes a script and/or dialogues tagged with character names along with auxiliary information. Each script includes a plurality of portrayals performed by an associated actor or character. Linguistic representations of content of the narrative files in both abstract and semantic forms is determined. The linguistic representations are connected to higher order representations and mental states. The linguistic representations are connected to behavior and action. Interplay between language constructs and demographics of content creators is analyzed. Content representations towards individuals/groups are adapted to reflect heterogeneity in preferences.

Abstract: A method for processing multiple intents from an audio stream in a virtual reality application may include multiple steps, including: receiving a stream of words as a first utterance; processing the first utterance before the stream of words is fully received; based on the processing, determining a first intent from the first utterance before the stream of words is fully received; determining occurrence of a pause after the first utterance; and receiving a second stream of words as a second utterance, the second stream being received after the determined pause.

Abstract: Typical graphical user interfaces and predefined data fields limit the interaction between a person and a computing system. An oral communication device and a data enablement platform are provided for ingesting oral conversational data from people, and using machine learning to provide intelligence. At the front end, an oral conversational bot, or chatbot, interacts with a user. On the backend, the data enablement platform has a computing architecture that ingests data from various external data sources as well as data from internal applications and databases. These data and algorithms are applied to surface new data, identify trends, provide recommendations, infer new understanding, predict actions and events, and automatically act on this computed information. The chatbot then provides audio data that reflects the information computed by the data enablement platform. The system and the devices, for example, are adaptable to various industries.

Abstract: A speech interface device is configured to defer encryption of audio data on-device until a time when the encryption operation is not competing with other computationally-intensive operations for responding to the audio data. For example, audio data based on sound captured in an environment of the speech interface device can be stored in volatile memory of the speech interface device, without encrypting it, until a set of processing operations (e.g., ASR processing, NLU processing, audio event processing, etc.) performed based on the audio data have stopped. Based on a determination that these processing operations for responding to the audio data have stopped, the logic may encrypt the audio data to generate encrypted data, and the encrypted data can be stored in non-volatile memory of the speech interface device for uploading to a remote system when a connection is available.

Abstract: Systems and methods for sensitive data management are disclosed. A voice-enabled device may generate audio data representing a request from a user utterance. A remote system may perform speech-processing operations, including obtaining responsive text data from a third-party application. In examples, a sensitivity designation may be received from the third-party application, which may cause the remote system to encrypt the responsive text data, redact the text data, and/or remove the text data from the remote system after the response is provided to the voice-enabled device.

Abstract: Aspects of the disclosure relate to voice-based time-sensitive task processing over a high generation cellular network. A computing platform may establish a communication channel with a computing device. The computing platform may authenticate a user of the computing device, where the user is authorized to access the enterprise server. Then, the computing platform may detect, via the communication interface, a voice-based interaction from the authenticated user. The computing platform may cause the voice-based interaction to be captured as audio data. Subsequently, the computing platform may transform the audio data to textual data. The computing platform may analyze the textual data to identify a time-sensitive task related to an entity. Then, the computing platform may generate, based on the identified time-sensitive task, one or more instructions to execute the time-sensitive task.

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: Disclosed are various embodiments for delegated evaluation of a decision tree. A client device receives an expression and a plurality of responses from a second computing device, the expression providing an evaluation of a user input and each of the plurality of responses representing a potential response for the first computing device to provide to the user input. The client device then evaluates the user input with the expression received from the second computing device to determine a user intent. The client device then selects a response to the user intent from the plurality of responses and renders the response within a user interface.

Abstract: An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a model, wherein the model includes at least one original meaning representation. The system includes a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions including: performing rule-based generalization of the model to generate at least one generalized meaning representation of the model from the at least one original meaning representation of the model; performing rule-based refinement of the model to prune or modify the at least one generalized meaning representation of the model, or the at least one original meaning representation of the model, or a combination thereof; and after performing the rule-based generalization and the rule-based refinement of the model, using the model to extract intents/entities from a received user utterance.

Abstract: Provided are a method and an apparatus for providing an intelligent voice response at a voice assistant device. The method includes obtaining, by a voice assistant device, a voice input from a user, identifying non-speech input while obtaining the voice input, determining a correlation between the voice input and the non-speech input, and generating, based on the correlation, a response comprising an action related to the correlation or a suggestion related to the correlation.

Abstract: Methods, apparatuses, and computer program products are described herein that are configured to be embodied as a natural language document orchestrator. In some example embodiments, a method is provided that comprises generating natural language text. The method of this embodiment may also include executing document orchestration requests from a scripted input language file.

Abstract: In one aspect, a first device may include at least one processor, an ultra-wideband (UWB) transceiver accessible to the at least one processor, and storage accessible to the at least one processor. The storage may include instructions executable by the at least one processor to receive voice input at the first device and to track a second device using one or more UWB signals received from the second device via the UWB transceiver. The instructions may then be executable to, based on tracking, perform an operation using the voice input.

Abstract: A speech processing system uses contextual data to determine the specific domains, subdomains, and applications appropriate for taking action in response to spoken commands and other utterances. Some applications may be given priority over others such that some applications are general request applications to which responsibility for processing an intent is to be assigned as long as contextual criteria are satisfied, while other applications are specific request applications to which responsibility for processing an intent is to be assigned only if the applications are specifically requested, if the contextual criteria of priority applications are not satisfied, and/or if certain contextual criteria associated with the specific request applications are satisfied.

Abstract: A method of operating a voice assistant activation system, the voice assistant activation system comprising a microphone, a first voice assistant module, and a second voice assistant module. The method includes receiving, via the microphone, a voice input; transmitting the voice input via a first virtual microphone to the first voice assistant module; transmitting the voice input via a second virtual microphone to the second voice assistant module; activating the first voice assistant module when the voice input comprises a first keyword; and activating the second voice assistant module when the voice input comprises a second keyword.

Abstract: An apparatus for processing an audio signal having associated therewith a pitch lag information and a gain information, includes a domain converter for converting a first domain representation of the audio signal into a second domain representation of the audio signal; and a harmonic post-filter for filtering the second domain representation of the audio signal, wherein the post-filter is based on a transfer function including a numerator and a denominator, wherein the numerator includes a gain value indicated by the gain information, and wherein the denominator includes an integer part of a pitch lag indicated by the pitch lag information and a multi-tap filter depending on a fractional part of the pitch lag.

Abstract: A method comprises receiving at least one natural language input, converting the at least one natural language input to a graphical input, retrieving relationship data from a graph database based at least in part on the graphical input, and generating at least one natural language response of a virtual assistant to the at least one natural language input based at least in part on the relationship data from the graph database. At least the generating is performed using one or more machine learning models.

Abstract: In an approach to creation and execution of dialog shortcuts, responsive to detecting initiation of a dialog, an utterance is received from a user. Whether the utterance contains an objective of the user is determined, where the objective is chosen from a group including create a shortcut, execute the shortcut, modify the shortcut, and delete the shortcut. Responsive to determining that the utterance contains the objective, the objective is implemented.

Abstract: A processor for processing an audio signal has: an analyzer for deriving a window control signal from the audio signal indicating a change from a first asymmetric window to a second window, or indicating a change from a third window to a fourth asymmetric window, wherein the second window is shorter than the first window, or wherein the third window is shorter than the fourth window; a window constructor for constructing the second window using a first overlap portion of the first asymmetric window, wherein the window constructor is configured to determine a first overlap portion of the second window using a truncated first overlap portion of the first asymmetric window, or wherein the window constructor is configured to calculate a second overlap portion of the third window using a truncated second overlap portion of the fourth asymmetric window; and a windower for applying the first and second windows or the third and fourth windows to obtain windowed audio signal portions.

Abstract: The present disclosure relates to a device wakeup method and apparatus, an electronic device, and a storage medium. The wakeup method is applied to a first electronic device and includes: a wakeup message from a second electronic device is received, and when it is determined that a present state is an unawakened state, locally collected voice data is acquired; MFCC extraction is performed on the voice data to acquire a first MFCC of the voice data; the wakeup message is parsed to obtain a second MFCC included in the wakeup message; the first MFCC and the second MFCC are matched, and when it is determined that a difference between the first MFCC and the second MFCC is less than or equal to a set threshold value, a wakeup instruction is generated; and responsive to the wakeup instruction, the first electronic device is woken up.