Abstract: Disclosed is an electronic apparatus. The electronic apparatus includes: a communication interface, a memory, and a processor connected to the memory and the communication interface, the processor configured to control the electronic apparatus to, based on receiving a speech related to a function of the electronic apparatus, obtain text information corresponding to the received speech, control the communication interface to transmit the obtained text information to a server including a first neural network model corresponding to the function, execute the function based on response information received from the server, and based on identifying that an update period of the first neural network model is greater than or equal to a first threshold period based on the information related to the function of the electronic apparatus, the electronic apparatus may receive the information about the first neural network model from the server and store the information in the memory.

Abstract: A computer-implemented method for providing a trained function for performing a workpiece quality control includes receiving a plurality of training machining datasets, wherein different training high-frequency machining datasets are representative for the quality of different workpieces, transforming the plurality of training machining datasets into the time-frequency domain to generate a plurality of training time-frequency domain datasets, and training a function based on the plurality of training time-frequency domain datasets, wherein the function is based on an autoencoder. The autoencoder has input layers, output layers and a hidden layer. The plurality of training time-frequency domain datasets are provided to the input layers and the output layers during training, and a trained autoencoder function is outputted.

Abstract: A method, computer program product, and computing system for receiving feature-based voice data. One or more data augmentation characteristics may be received. One or more augmentations of the feature-based voice data may be generated, via a machine learning model, based upon, at least in part, the feature-based voice data and the one or more data augmentation characteristics.

Abstract: A natural language identity classifier system is described, which employs a supervised machine learning (ML) model to perform language identity classification on input text. The ML model takes, as input, non-lexicalized features of target text derived from subword tokenization of the text. Specifically, these non-lexicalized features are generated based on statistics determined for tokens identified for the input text. According to an embodiment, at least some of the non-lexicalized features are based on natural language-specific summary statistics that indicate how often tokens were found within a corpus for each natural language. Use of such summary statistics allows for generation of natural language specific conditional probability-based features.

Abstract: In this disclosure, the energy management problem of the D-LEGS with FFR is analyzed, so as to enhance the frequency stability of the main grid. The post-disturbance frequency response behaviors of both the main grid and the D-IEGS are precisely depicted, where the dead zones, limiting ranges and time constant of the governors are considered. The frequency regulation units of the D-IEGS include GTs and P2G units, whose impacts of providing frequency regulation service on the gas networks are quantified. Considering the time-scale similarity of the frequency dynamics and the dynamics of the GDN, the gas flow dynamics model is adopted. The frequency response dynamics of the GTs and P2G units, and the gas flow dynamics of the GDN, a variable-step difference scheme and a binary variable reduction method are devised.

Abstract: A system and method for authenticating an identity may include generating a first generic representation representing a stored audio content, generating a second generic representation representing input audio content, and, providing the first and second generic representations to a voice biometrics unit adapted to authenticate an identity based on the first and second generic representations.

Abstract: A system includes a unit of building equipment serving a building and including a heating, ventilation, or cooling component and onboard circuitry configured to execute a configuration routine stored on the circuitry which automatically configures parameters of the rooftop unit based on the signals received from sensors and devices at the building. The system also includes a cloud system communicably connectable to the onboard circuitry and configured to influence the configuration routine.

Abstract: FIG. 1 illustrates a decoder for decoding a current frame to reconstruct an audio signal according to an embodiment. The audio signal is encoded within the current frame. The current frame includes a current bitstream payload. The current bitstream payload includes a plurality of payload bits. The plurality of payload bits encodes a plurality of spectral lines of a spectrum of the audio signal. Each of the payload bits exhibits a position within the current bitstream payload. The decoder includes a decoding module and an output interface. The decoding module is configured to reconstruct the audio signal. The output interface is configured to output the audio signal.

Abstract: A search apparatus includes a processor and a memory. The processor receives a molding result of a reference sample manufactured by the additive manufacturing apparatus. The processor calculates predicted values from a predictive model. The processor determines whether the evaluation target values are achieved by the measured values. The reference sample has at least three smooth surfaces and a surface having aggregated punched holes formed by straight lines and curved lines that are involved in three types of regions to be set as the conditions.

Abstract: Devices and techniques are generally described for adaptive targeting for voice notifications. In various examples, first data representing a predicted likelihood that a first user will interact with first content within a predefined amount of time may be received. A first set of features including features related to past voice notifications sent to the first user may be determined. A second set of features including features related to interaction with the first content when past voice notifications were sent may be received. A first machine learning model may generate a prediction that a voice notification will increase a probability that the first user interacts with the first content based on the first data, the first set of features, and the second set of features. Audio data comprising the voice notification may be sent to a first device associated with the first content.

Abstract: Examples relate to on-device non-semantic representation fine-tuning for speech classification. A computing system may obtain audio data having a speech portion and train a neural network to learn a non-semantic speech representation based on the speech portion of the audio data. The computing system may evaluate performance of the non-semantic speech representation based on a set of benchmark tasks corresponding to a speech domain and perform a fine-tuning process on the non-semantic speech representation based on one or more downstream tasks. The computing system may further generate a model based on the non-semantic representation and provide the model to a mobile computing device. The model is configured to operate locally on the mobile computing device.

Abstract: A computer-implemented method is provided of using a machine learning model for disentanglement of prosody in spoken natural language. The method includes encoding, by a computing device, the spoken natural language to produce content code. The method further includes resampling, by the computing device without text transcriptions, the content code to obscure the prosody by applying an unsupervised technique to the machine learning model to generate prosody-obscured content code. The method additionally includes decoding, by the computing device, the prosody-obscured content code to synthesize speech indirectly based upon the content code.

Abstract: An apparatus for providing a processed audio signal representation on the basis of input audio signal representation configured to apply an un-windowing, in order to provide the processed audio signal representation on the basis of the input audio signal representation. The apparatus is configured to adapt the un-windowing in dependence on one or more signal characteristics and/or in dependence on one or more processing parameters used for a provision of the input audio signal representation.

Abstract: A direct speech-to-speech translation (S2ST) model includes an encoder configured to receive an input speech representation that to an utterance spoken by a source speaker in a first language and encode the input speech representation into a hidden feature representation. The S2ST model also includes an attention module configured to generate a context vector that attends to the hidden representation encoded by the encoder. The S2ST model also includes a decoder configured to receive the context vector generated by the attention module and predict a phoneme representation that corresponds to a translation of the utterance in a second different language. The S2ST model also includes a synthesizer configured to receive the context vector and the phoneme representation and generate a translated synthesized speech representation that corresponds to a translation of the utterance spoken in the different second language.

Abstract: Disclosed embodiments provide techniques to identify the in-context meanings of natural language in order to decipher the evolution or creation of new vocabulary words and create a more holistic user experience. Thus, disclosed embodiments improve the technical field of digital content comprehension. In embodiments, machine learning is used to identify sentiment of text, perform entity detection to determine topics of text, and/or perform image analysis on images used in digital content. Words, symbols, and images that are determined to be potentially unfamiliar to a user are augmented with a supplemental definition indication. Invoking the supplemental definition indication enables rendering of additional definition information for the user. This serves to accelerate understanding of digital content such as webpages and social media posts.

Abstract: A non-transitory computer readable storage medium has instructions executed by a processor to invoke an image processing module to ingest a digital invoice. An evaluation module derives metrics from the digital invoice. A semantic document processing module forms entity extracts from the digital invoice, where each entity extract from the digital invoice has a potential mapping to a trained machine learning model element. An entity extraction correction module overrides the potential mapping to the trained machine learning model element when user feedback from a similar entity extract from a previously processed digital invoice exists to produce a processed digital invoice with a user feedback element inconsistent with the potential mapping to the trained machine learning model element. The processed digital invoice is delivered to an accounting module for final disposition of the digital invoice.

Abstract: The present disclosure discloses a context-based multi-turn dialogue method.

Abstract: A method and system for identifying the beginning and ending of songs via a machine learning analysis. A machine learning model analyzes streaming audio (such as a radio broadcast) in overlapping, 3-second samples. Each sample is labeled into groups such as “song,” “talk,” “commercial” and “transition.” Based on the location of the transition samples, an exact second a given song begins and ends in the audio stream is derivable. The model further identifies when two songs shift between one another.

Abstract: Methods and systems for multimodal conversational dialogue. The multimodal conversational dialogue system includes multiple sensors to detect multimodal inputs from a user. The multimodal conversational dialogue system includes a multimodal sematic parser that performs semantic parsing and multimodal fusion of the multimodal inputs to determine a goal of the user. The multimodal conversational dialogue system includes a dialogue manager that generates a dialogue with the user in real-time. The dialogue includes system-generated utterances that are used to conduct a conversation between the user and the multimodal conversational dialogue system.

Abstract: A machine translation system, a ChatOps system, a method for a context-aware language machine identification, and computer program product. One embodiment of the machine translation system may include a density calculator. The density calculator may be adapted to calculate a part of speech (POS) density for a plurality of word tokens in an input text, calculate a knowledge density for the plurality of word tokens, and calculate an information density for the plurality of word tokens using the POS density and the knowledge density. In some embodiments, the machine translation system may further comprise a sememe attacher and a context translator.