Abstract: A computing device is disclosed which includes a processor and non-transient memory operably connected to the processor. The non-transient memory includes instructions that, when executed by the processor cause the processor to extract a plurality of sub-strings from a character string, analyze each sub-string for compliance with each of several field definitions, where each of the field definitions corresponds to a field in a digital form, and populate some of the fields in the digital form based on the analysis of each sub-string for compliance with the field definitions.

Abstract: An electronic system is provided. The electronic system includes a host and a display. The host includes an audio processing module, and a smart interpreter engine. The audio processing module is utilized for acquiring audio data corresponding to a first language from audio streams processed by an application program executed on the host. The application program executed on the host includes a specific game software. The smart interpreter engine is utilized for receiving the audio data corresponding to the first language from the audio processing module and converting the audio data corresponding to the first language into text data corresponding to a second language according to the game software executed on the host The display is utilized for receiving the text data corresponding to the second language from the smart interpreter engine and displaying the text data corresponding to the second language.

Abstract: Systems and methods are disclosed herein for training a model to accurately determine whether two phrases are conversationally connected. A media guidance application may detect a first phrase and a second phrase, translate each phrase to a string of word types, append each string to the back of a prior string to create a combined string, determine a degree to which any of the individual strings matches any singleton template, and determine a degree to which the combined string matches any conversational template. Based on the degrees to which the individual and combination strings match the singleton and conversational templates, respectively, strengths of association are correspondingly updated.

Abstract: A method for detecting freeze words includes receiving audio data that corresponds to an utterance spoken by a user and captured by a user device associated with the user. The method also includes processing, using a speech recognizer, the audio data to determine that the utterance includes a query for a digital assistant to perform an operation. The speech recognizer is configured to trigger endpointing of the utterance after a predetermined duration of non-speech in the audio data. Before the predetermined duration of non-speech, the method includes detecting a freeze word in the audio data. In response to detecting the freeze word in the audio data, the method also includes triggering a hard microphone closing event at the user device. The hard microphone closing event prevents the user device from capturing any audio subsequent to the freeze word.

Abstract: Methods, systems, and apparatus for receiving audio data corresponding to a user utterance and context data, identifying an initial set of one or more n-grams from the context data, generating an expanded set of one or more n-grams based on the initial set of n-grams, adjusting a language model based at least on the expanded set of n-grams, determining one or more speech recognition candidates for at least a portion of the user utterance using the adjusted language model, adjusting a score for a particular speech recognition candidate determined to be included in the expanded set of n-grams, determining a transcription of user utterance that includes at least one of the one or more speech recognition candidates, and providing the transcription of the user utterance for output.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. In accordance with one example, a method includes, at an electronic device with one or more processors and memory, receiving a first natural-language speech input indicative of a request for media, where the first natural-language speech input comprises a first search parameter; providing, by a digital assistant, a first media item identified based on the first search parameter. The method further includes, while providing the first media item, receiving a second natural-language speech input and determining whether the second input corresponds to a user intent of refining the request for media. The method further includes, in accordance with a determination that the second speech input corresponds to a user intent of refining the request for media: identifying, based on the first parameter and the second speech input, a second media item and providing the second media item.

Abstract: An audio encoder for providing an output signal using an input audio signal includes a patch generator, a comparator and an output interface. The patch generator generates at least one bandwidth extension high-frequency signal, wherein a bandwidth extension high-frequency signal includes a high-frequency band. The high-frequency band of the bandwidth extension high-frequency signal is based on a low frequency band of the input audio signal. A comparator calculates a plurality of comparison parameters. A comparison parameter is calculated based on a comparison of the input audio signal and a generated bandwidth extension high-frequency signal. Each comparison parameter of the plurality of comparison parameters is calculated based on a different offset frequency between the input audio signal and a generated bandwidth extension high-frequency signal.

Abstract: An audio encoder for providing an output signal using an input audio signal includes a patch generator, a comparator and an output interface. The patch generator generates at least one bandwidth extension high-frequency signal, wherein a bandwidth extension high-frequency signal includes a high-frequency band. The high-frequency band of the bandwidth extension high-frequency signal is based on a low frequency band of the input audio signal. A comparator calculates a plurality of comparison parameters. A comparison parameter is calculated based on a comparison of the input audio signal and a generated bandwidth extension high-frequency signal. Each comparison parameter of the plurality of comparison parameters is calculated based on a different offset frequency between the input audio signal and a generated bandwidth extension high-frequency signal.

Abstract: An electronic device includes a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to receive audio data corresponding to a user's utterance, to identify the user's utterance characteristics based on the received audio data, to determine a parameter for performing voice activity detection, by using the identified user's utterance characteristics, and to perform voice activity detection on the received audio data with respect to the user's utterance by using the determined parameter.

Abstract: An audio encoder for providing an output signal using an input audio signal includes a patch generator, a comparator and an output interface. The patch generator generates at least one bandwidth extension high-frequency signal, wherein a bandwidth extension high-frequency signal includes a high-frequency band. The high-frequency band of the bandwidth extension high-frequency signal is based on a low frequency band of the input audio signal. A comparator calculates a plurality of comparison parameters. A comparison parameter is calculated based on a comparison of the input audio signal and a generated bandwidth extension high-frequency signal. Each comparison parameter of the plurality of comparison parameters is calculated based on a different offset frequency between the input audio signal and a generated bandwidth extension high-frequency signal.

Abstract: A vehicle terminal system for processing a message includes: a portable device for receiving the message; and a vehicle terminal for analyzing a text of the message received from the portable device to yield an analysis result, and for determining a recommended operation corresponding to the analysis result. Thus, the vehicle terminal does not output pop-up images for all messages received from the portable device and recommends a message suitable for the user.

Abstract: Leveraging domain knowledge is an effective strategy for enhancing the quality of inferred low-dimensional representations of documents by topic models. Presented herein are embodiments of a Bayesian nonparametric model that employ knowledge graph (KG) embedding in the context of topic modeling for extracting more coherent topics; embodiments of the model may be referred to as topic modeling with knowledge graph embedding (TMKGE). TMKGE embodiments are hierarchical Dirichlet process (HDP)-based models that flexibly borrow information from a KG to improve the interpretability of topics. Also, embodiments of a new, efficient online variational inference method based on a stick-breaking construction of HDP were developed for TMKGE models, making TMKGE suitable for large document corpora and KGs. Experiments on datasets illustrate the superior performance of TMKGE in terms of topic coherence and document classification accuracy, compared to state-of-the-art topic modeling methods.

Abstract: An audio encoder for providing an output signal using an input audio signal includes a patch generator, a comparator and an output interface. The patch generator generates at least one bandwidth extension high-frequency signal, wherein a bandwidth extension high-frequency signal includes a high-frequency band. The high-frequency band of the bandwidth extension high-frequency signal is based on a low frequency band of the input audio signal. A comparator calculates a plurality of comparison parameters. A comparison parameter is calculated based on a comparison of the input audio signal and a generated bandwidth extension high-frequency signal. Each comparison parameter of the plurality of comparison parameters is calculated based on a different offset frequency between the input audio signal and a generated bandwidth extension high-frequency signal.

Abstract: An information processing apparatus 10 includes an acceptance unit 12, a formal language generation unit 14, an inference unit 16, and an update unit 18. The formal language generation unit 14 generates training expressions in a formal language based on parameters prepared in advance and pieces of text accepted by the acceptance unit 12. The inference unit 16 executes at least one inference out of deductive inference and abduction on the above training expressions using a knowledge base prepared in advance. The update unit 18 compares an inference result of the inference unit 16 with a model answer input in advance, and updates parameters such that an inference result that matches the model answer is likely to be obtained through inference performed by the inference unit 16.

Abstract: An audio encoder for providing an output signal using an input audio signal includes a patch generator, a comparator and an output interface. The patch generator generates at least one bandwidth extension high-frequency signal, wherein a bandwidth extension high-frequency signal includes a high-frequency band. The high-frequency band of the bandwidth extension high-frequency signal is based on a low frequency band of the input audio signal. A comparator calculates a plurality of comparison parameters. A comparison parameter is calculated based on a comparison of the input audio signal and a generated bandwidth extension high-frequency signal. Each comparison parameter of the plurality of comparison parameters is calculated based on a different offset frequency between the input audio signal and a generated bandwidth extension high-frequency signal.

Abstract: With the proliferation of data and documents available on the internet and other information sources, analysis of adverse events poses a serious technical challenge on account of associated data volume and variety. This disclosure relates generally to identification and profiling of adverse events. By receiving a set of articles from a plurality of data sources and utilizing a series of Natural Language Processors, NLP techniques are employed to identify implicit and explicit adverse events. Entity statistics and sentiment extraction and analysis is performed. An ontology based adverse event identification framework is proposed for identification and profiling of implicit adverse event. An attention based bi-directional long short term memory network for adverse event identification and classification is proposed.

Abstract: An audio signal processor includes a digital filter block configured to receive an audio signal and output a first filtered audio signal, and a phase linearization block configured to receive the first filtered audio signal and output a second filtered audio signal with a more linear phase.

Abstract: The dialog device according to the present invention includes a prediction unit 254 configured to predict an utterance length attribute of a user utterance in response to a the machine utterance, a selection unit 256 configured to use the utterance length attribute to select, as a feature model for usage in an end determination of the user utterance, at least one of an acoustic feature model or a lexical feature model, and an estimation unit 258 configured to estimate an end point in the user utterance using the selected model. By using this dialog device, it is possible to shorten the waiting time until a response is output to a user utterance by a machine, and to realize a more natural conversation between a user and a machine.

Abstract: A method, performed by the electronic device, of providing a voice recognition service includes obtaining a user call keyword for activating the voice recognition service, based on a first user voice input; generating a user-customized voice database (DB) by inputting the obtained user-customized keyword to a text to speech module; and obtaining a user-customized feature by inputting an audio signal of the user-customized voice DB to a pre-trained wake-up recognition module.

Abstract: Method includes receiving, through a plurality of channels, audio data corresponding to a plurality of frequency ranges; determining, for each channel's frequency ranges, speech audio and/or noise energy level using a model trained by machine learning; determining a speech signal with removed noise for each channel; determining one or more statistical values associated with an energy level of a channel's speech signal with the removed noise; determining a strongest channel that has highest statistical values associated with an energy level of a speech signal; determining that the one or more statistical values associated with the energy level of the strongest channel's speech signal satisfy a threshold condition; comparing statistical values associated with an energy level of a speech signal of each channel with those of the strongest channel; and determining whether to update a gain value for a channel based on the channel's statistical values associated with the energy level.