Abstract: Systems and processes for providing differentiated advertising sponsorship of a fabricated reading product are provided. Natural language digital text characterized by a sentence, comprised of words is user selected. The text is linguistically analyzed in furtherance of displaying a fabricated reading product corresponding to the text. The words of the sentence of the text, and the sentences, are evaluated with regard to word/sentence attributes for the words of the sentences text in furtherance of supplying only the word/sentence attributes to an advertising sponsor. Based upon either or both of the word and sentence evaluation, determining whether to supply an ad from the advertising sponsor in relation to a display of the fabricated reading product, and, in connection to supplying an ad, further determining placement position of the ad in relation to the display of the fabricated reading product.

Abstract: Systems, methods and programmed products for using visual information in a video stream of a recording streaming teleconference among a plurality of participants to diarize speech, involving obtaining respective components of the teleconference including a respective audio component, a respective video component, respective teleconference metadata, and transcription data, parsing components into speech segments, tagging speech segments with source feeds, and diarizing the teleconference so as to label the speech segments based on neural network or heuristic analysis of visual information.

Abstract: Various aspects of the present disclosure are directed to a process for modifying an audio signal. For example, one process for modifying an audio signal is disclosed including the following steps: determining a compression parameter of the audio signal that should be modified; fractionizing the audio signal into different frequency bands; obtaining the values of the compression parameter for each frequency band; and compressing at least a part of the frequency bands as a function of the determined compression parameter. Various other embodiments of the present disclosure are directed to a device for modifying an audio signal.

Abstract: Methods, apparatuses, and computer program products for multilingual simultaneous interpretation using a distributed ledger are disclosed. A multilingual interpretation server determines that a new word has been added to a first language node, the first language node corresponding to a first language, and broadcasts, to a plurality of other language nodes, a request to interpret the new word, wherein each of the plurality of other language nodes corresponds to a different language. Each of the plurality of language nodes interprets the new word into a particular language and adds the new word and one or more interpretations of the new word to an entry in a dictionary ledger. A multilingual interpretation service provides simultaneous multilingual translations of from a source language to a plurality of target languages using the shared distributed dictionary ledger. A multilingual interpretation client is provided for accessing services provided by the multilingual interpretation service.

Abstract: A method executed by a computing device includes determining a set of identigens for each query word of a query to produce sets of identigens, where a set of identigens represents different meanings of a word of the query. The method further includes obtaining a first identigen selection for a first query word from the first set of identigens. The method further includes interpreting, using identigen pairing rules and based on the first identigen selection, the sets of identigens to produce a query entigen group. The method further includes accessing a knowledge database utilizing the query entigen group to produce a response entigen group. The method further includes generating a response to the query using the response entigen group, where the response includes at least one response word.

Abstract: Various embodiments of a system and associated method for detecting and localizing gunshots are disclosed herein.

Abstract: Method and system of securing personally identifiable and sensitive information in conversational AI based communication. The method comprises enabling a first service provider device as a communication channel provider of an incoming communication mode and enabling a second service provider device s a communication channel provider of an outgoing communication mode, at least one of the incoming communication and outgoing communication modes comprising an audio communication, storing content of a conversation in the incoming communication mode in a first storage medium accessible to the first service provider device but not the second service provider device, and storing content of the conversation in the outgoing communication mode at a second storage medium accessible to the second service provider device but not the first service provider device, and anonymizing the audio communication wherein personally identifiable audio characteristics of the user are obfuscated from the service provider devices.

Abstract: A system and method to perform dubbing automatically for multiple languages at the same time using speech-to-text transcriptions, language translation, and artificial intelligence engines to perform the actual dubbing in the voice likeness of the original speaker.

Abstract: Systems and methods for natural language processing are described. Embodiments of the inventive concept are configured to receive an input sequence and a plurality of candidate long forms for a short form contained in the input sequence, encode the input sequence to produce an input sequence representation, encode each of the plurality of candidate long forms to produce a plurality of candidate long form representations, wherein each of the candidate long form representations is based on a plurality of sample expressions and each of the sample expressions includes a candidate long form and contextual information, compute a plurality of similarity scores based on the candidate long form representations and the input sequence representation, and select a long form for the short form based on the plurality of similarity scores.

Abstract: A method including detecting a webpage accessed by a user on a computing device via a browser. Content on the webpage is determined, and a model is applied to the content to determine a plurality of keyword sets. A network search is performed based on each of the plurality of keyword sets to generate a plurality of search results. The plurality of search results are compared to the content, and the plurality of search results are compared to each other. A factualness of the content is determined based on the comparing of the plurality of search results to the content and based on the comparing of the plurality of the search results to each other, and the user is notified via the browser of the factualness of the content.

Abstract: A method of providing sentiment analysis includes training a computer system to identify a polarity of a sentiment for a plurality of phrases and receiving, at the computer system, an input from a website. The input includes a text string including a phrase. The method includes determining, by the computer system, the polarity of the sentiment of the phrase by comparing the phrase to the plurality of phrases and sending, by the computer system, a command to the website that causes the website to display predetermined content based on the determined polarity of the sentiment of the phrase.

Abstract: A back-end server system for step-up authentication for conversational interfaces using spoken passphrases receive a conversational request for an action which requires step-up authorization; respond to the user through conversational artificial intelligence (AI) warning the user that an additional step is required; transmit, over a dedicated communication channel to the user and different than the conversational interface, a secret passphrase generated using a spoken passphrase dictionary; receive, by the conversational interface, a spoken submission from the user in response to the warning; confirm validity of the received spoken submission by comparing the spoken submission to the passphrase; and in response to confirmation of the spoken submission, authorize the requested action.

Abstract: In some examples, a computing device may implement a method that includes receiving microservice profile information at a microservice profiler, performing lexical analysis of the microservice profile information (where the lexical analysis produces tokenized information), generating microservice modification information by performing machine learning analysis of one or more inputs (where the one or more inputs comprise the tokenized information), and outputting the microservice modification information from the microservice profiler. The microservice profile information describes one or more characteristics of a microservice. The lexical analysis is performed by a lexical analysis engine of the microservice profiler, and the machine learning analysis is performed by a machine learning system of the microservice profiler.

Abstract: Systems may perform analyses of claims included in a patent document. The systems may generate one or more search strings from the patent document and provide the one or more search strings to a third-party searching authority. The third-party searching authority may return a collection of documents responsive to the one or more search strings. In particular situations, the systems may re-rank the documents of the collection to provide a patent centric ranking. The systems may also analyze the documents of the collection with respect to the elements of the claims to generate various types of patent infringement and/or invalidity reports.

Abstract: Described are systems, methods, apparatuses, and computer program product embodiments for automatically processing intent-based spoken language for SLU. The disclosed solution uses a scale-free network structured conversational knowledge graph that stores nodes representative of actions, objects, and intent names and edges representative of relationships between the nodes. For all phrases (including a sentence) from the same intent, the system calculates a mean feature vector using a Universal Sentence Embedding (USE) model as a feature element. The system also employs a multi-step intent detection strategy. A graph query technique may be used to match all potential intent nodes from the trained knowledge graph. The system may compute a covariance matrix between the feature element of an input phrase and feature elements of all potential intents. The major component of the covariance matrix along with the maximum covariance may be used to determine the final intent.

Abstract: The learning data generation device (10) of the present invention comprises: an end-of-talk predict unit (11) for performing: a first prediction in which it is predicted, based on utterance information on an utterance in the dialog, using the end-of-talk prediction model (16), whether the utterance is an end-of-talk utterance of the speaker; and a second prediction in which it is predicted, based on one or more prescribed rules, whether the utterance is an end-of-talk utterance; and a training data generate unit (13) for generating, when, in the first prediction it is predicted that the utterance is not an end-of-talk utterance and in the second prediction it is predicted that the utterance is an end-of-talk utterance, for the utterance information on the utterance, learning data to which training data indicating that the utterance is an end-of-talk utterance is appended.

Abstract: A conference terminal and an embedding method of audio watermarks are provided. In the method, a first speech signal and a first audio watermark signal are received respectively. The first speech signal relates to a speaker corresponding to another conference terminal, and the first audio watermark signal corresponds to the another conference terminal. The first speech signal is assigned to a host path to output a second speech signal. The first audio watermark signal is assigned to an offload path to output a second audio watermark signal. The host path provides more digital signal processing (DSP) effects than the offload path. The second speech signal and the second audio watermark signal are synthesized to output a synthesized audio signal. The synthesized audio signal is adapted for audio playback. A completed audio watermark signal is outputted accordingly.

Abstract: According to an aspect, method for synthesizing multi-speaker speech using an artificial neural network comprises generating and storing a speech learning model for a plurality of users by subjecting a synthetic artificial neural network of a speech synthesis model to learning, based on speech data of the plurality of users, generating speaker vectors for a new user who has not been learned and the plurality of users who have already been learned by using a speaker recognition model, determining a speaker vector having the most similar relationship with the speaker vector of the new user according to preset criteria out of the speaker vectors of the plurality of users who have already been learned, and generating and learning a speaker embedding of the new user by subjecting the synthetic artificial neural network of the speech synthesis model to learning, by using a value of a speaker embedding of a user for the determined speaker vector as an initial value and based on speaker data of the new user.

Abstract: The present invention provides for improving training dataset by identifying errors in training dataset and generating improvement recommendations. In operation, the present invention provides for identifying and correcting duplicate utterances in training dataset comprising utterances-intent pairs. Further, a plurality of Natural Language ML models are trained with the corrected training dataset to obtain diverse set of trained ML models. Each utterance of training dataset are fed as input to trained ML models, and a probability of error associated with each utterances-intent pairs of training dataset are evaluated based on analysis of respective intent predictions received from each of the trained ML models. Furthermore, spelling errors in the dataset are identified and data-imbalances in the training dataset are evaluated.

Abstract: A multi-channel signal encoding method includes determining a downmixed signal of a first channel signal and a second channel signal, determining an initial reverberation gain parameter of the first channel signal and the second channel signal, determining a target reverberation gain parameter of the first channel signal and the second channel signal based on a correlation between the first channel signal and the downmixed signal, a correlation between the second channel signal and the downmixed signal, and the initial reverberation gain parameter, quantizing the first channel signal and the second channel signal based on the downmixed signal and the target reverberation gain parameter, and writing a quantized first channel signal and a quantized second channel signal into a bitstream.